Dissertationen: „Model predictive controller (MPC)“

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Bangalore, Narendranath Rao Amith Kaushal. „Online Message Delay Prediction for Model Predictive Control over Controller Area Network“. Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.

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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage.
Master of Science

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Mattsson, Mathias, und Rasmus Mehler. „Optimal Vehicle Speed Control Using a Model Predictive Controller for an Overactuated Vehicle“. Thesis, Linköpings universitet, Fordonssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119480.

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To control the speed of an overactuated vehicle there may be many possible ways to use the actuators of the car achieving the same outcome. The actuators in an ordinary car is a combustion engine and a friction brake. In some cases it is trivial how to coordinate actuators for the optimal result, but in many cases it is not. The goal with the thesis is to investigate if it is possible to achieve the same or improved performance with a more sophisticated control structure than today's, using a model predictive controller. A model predictive controller combines the possibility to predict the outcome through an open-loop controller with the stability of a closed loop controller and gives the optimal solution for a finite horizon optimization problem. A simple model of the longitudinal dynamics of a car is developed and used in the model predictive controller framework. This is then used in simulations and in a real car. It is shown that it is possible to replace the current controller structure with a model predictive controller, but there are advantages and disadvantages with the new control structure.

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Claro, Érica Rejane Pereira. „Localização de canais afetando o desempenho de controladores preditivos baseados em modelos“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/149927.

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O escopo desta dissertação é o desenvolvimento de um método para detectar os modelos da matriz dinâmica que estejam degradando o desempenho de controladores preditivos baseados em modelos. O método proposto se baseia na análise de correlação cruzada entre o erro nominal do controlador em malha fechada e a uma estimativa da contribuição de cada canal para o cálculo da saída, filtrada pela função de sensibilidade do controlador. Esse método pode ser empregado na auditoria de controladores com variáveis controladas em setpoints e/ou com variáveis que operem entre faixas, como é usual de se encontrar na indústria. Esta dissertação apresenta os resultados da aplicação bem sucedida do método no sistema de quatro tanques (JOHANSSON, 2000), para o qual três cenários foram avaliados. No primeiro cenário, o método localizou corretamente discrepâncias de ganho e de dinâmica de modelos de um controlador preditivo baseado em modelos (Model-based Predictive Controller, ou controlador MPC). No segundo, o método foi utilizado para avaliar a influência de uma variável externa para melhorar o desempenho de um controlador afetado por distúrbios não medidos. No terceiro cenário, o método localizou canais com modelos nulos que deveriam ser incluídos na matriz de controle de um controlador MPC de estrutura descentralizada. Os resultados deste estudo de caso foram comparados com aqueles obtidos pelo método proposto por BADWE, GUDI e PATWARDHAN (2009), constatando-se que o método proposto é mais robusto que o método usado na comparação, não demandando ajustes de parâmetros por parte do usuário para fornecer bons resultados. A dissertação inclui também um estudo de caso da aplicação industrial do método na auditoria de desempenho de um controlador preditivo linear de estrutura descentralizada, com doze variáveis controladas, oito manipuladas e quatro distúrbios não medidos, aplicado a um sistema de fracionamento de propeno e propano em uma indústria petroquímica. A auditoria permitiu reduzir o escopo de revisão do controlador a dezenove canais da matriz, sendo que quatorze destes correspondiam a canais com modelos nulos que deveriam ser incluídos na matriz. A eficácia do método foi comprovada repetindo-se a avaliação da qualidade de modelo para todas as variáveis controladas.
The scope of this dissertation is the development of a method to detect the models of the dynamic matrix that are affecting the performance of model-based predictive controllers. The proposed method is based on the cross correlation analysis between the nominal controller error and an estimate of the contribution of each channel to the controller output, filtered by the controller nominal sensitivity function. The method can be used in the performance assessment of controllers employing variables controlled at the setpoint and/or those controlled within ranges. This dissertation presents the results of the successful application of the method to the quadruple-tank process (JOHANSSON, 2000), for which three scenarios were evaluated. In the first scenario, the method correctly located gain and dynamic mismatches on a model-based predictive controller (MPC controller). In the second one, the method was used to evaluate the influence of an external variable to improve the performance of a controller affected by unmeasured disturbances. In the third scenario, the method located null models that should be included in the dynamic matrix of a decentralized MPC controller. The results of the three scenarios were compared with the ones obtained through the method proposed by BADWE, GUDI e PATWARDHAN (2009). The proposed method was considered more robust than the reference one for not requiring parameters estimation performed by the user to provide good results. This dissertation also includes a case study about the application of the method on the performance assessment of an industrial linear predictive controller of decentralized structure. The controller has twelve controlled variables, eight manipulated variables, and four unmeasured disturbances and is applied to a propylene-propane fractionation system of a petrochemical industry. The performance assessment allowed reducing the scope of the controller revision to nineteen channels of the models matrix, fourteen of which were null models that should be included in the controller. The efficacy of the proposed method was confirmed by repeating the model quality evaluation for all the controlled variables.

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Paula, Neander Alessandro da Silva. „MPC adaptativo - multimodelos para controle de sistemas não-lineares“. Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-14052009-000836/.

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Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do melhor modelo o qual pertence a um conjunto previamente levantado. A metodologia proposta considera um controlador em duas camadas e a excitação do processo através de um sinal GBN na camada de otimização com o controlador em malha fechada. Está sendo considerada a validação deste controlador adaptativo através da comparação dos resultados com duas diferentes técnicas Controlador MMPC e Identificação ARX, para a comprovação dos bons resultados desta metodologia.
During the operation of a MPC, the plant can change the operation point mainly due to management decision or due to the presence of measured or unmeasured disturbances. Thus, the model of the controller must be adapted to improve the control in the new operation conditions. In such a way, a better control policy can be achieved if a large number of models are identified at the possible operation points and it is available an adaptive controller that is capable of selecting the best model. In this work is presented a methodology of adaptive control with on-line identification of the most adequate model which belongs to a set of models previously obtained. The proposed methodology considers a two-layer controller and process excitation by a GBN signal in the LP optimization layer with the controller in closed loop mode. It is also presented the adaptive controller validation by comparing the proposed approach with two different techniques - MMPC and ARX Identification, to confirm the good results with this new methodology to the adaptive controller.

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Venieri, Giulia. „Development and testing of Model Predictive Controllers for an automotive organic Rankine cycle unit“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Two-thirds of the energy produced by an internal combustion engine (ICE) is lost into waste heat through the coolant and the exhaust gas; hence, studying Waste Heat Recovery (WHR) systems is of vital importance. The organic Rankine cycle (ORC) is a powerful system to recover low-grade heat and transform it into electrical energy. This thesis aimed at developing and testing a Model Predictive Control (MPC) system that ensures a safe operation of a system that constitutes an ICE bottomed by an ORC unit. The experimentation was carried out at the DTU Mekanik laboratories and was divided into different campaigns. Firstly, to study the plant behavior, steady-state and dynamic characterizations were accomplished. The latter was useful to obtain transfer function models for the MPCs at different vehicle speeds. Secondly, Proportional-Integral (PI) controllers and MPCs qualities were evaluated thanks to three performance indices while the engine was following a testing cycle. The MPC model was derived at 90km/h. Afterward, a test campaign aimed at optimizing the tuning parameters of the MPC cost function and at evaluating their influence on the plant response. Finally, the controllers that performed best were tested on a World harmonized Light-duty vehicles Testing Cycle (WLTC) to characterize their operation under realistic driving conditions. The results showed that MPCs were more suitable for the task than PIs due to their better ability to operate the plant in safe conditions, and to their best performance indices when subjected to the testing cycles as well as to the WLTC. Nevertheless, MPCs have to be further optimized to follow the homologation cycle. Future experimentations could be based on be exploiting multi-model systems constituted of two or more MPCs or obtaining the MPC model from other working points.

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Andina, Elisa. „Complexity and Conservatism in Linear Robust Adaptive Model Predictive Control“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Questa tesi presenta uno schema di controllo robusto adattativo basato sulla tecnica di controllo avanzato model predictive control (MPC) per sistemi lineari soggetti a disturbi additivi e incertezze parametriche, costanti e variabili. L'approccio proposto fornisce uno schema di controllo efficiente dal punto di vista computazionale con stima dei parametri online per ottenere un aumento delle prestazioni e una diminuzione progressiva del conservatismo. L'insieme dei parametri è estimato usando una tecnica di identificazione a finestra mobile per ottenere un insieme con complessità limitata. Il soddisfacimento robusto dei vincoli è ottenuto tramite la tecnica di controllo robusto tube based MPC, mentre la stabilità L2 dello schema ad anello chiuso è assicurata utilizzando una stima dei parametri ottenuta con l'algoritmo least mean squares (LMS) nella funzione di costo. Con un esempio infine viene studiato il compromesso tra complessità e conservatismo di tale schema di controllo efficiente dal punto di vista computazionale.

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Rokebrand, Luke Lambertus. „Towards an access economy model for industrial process control“. Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/79650.

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With the ongoing trend in moving the upper levels of the automation hierarchy to the cloud, there has been investigation into supplying industrial automation as a cloud based service. There are many practical considerations which pose limitations on the feasibility of the idea. This research investigates some of the requirements which would be needed to implement a platform which would facilitate competition between different controllers which would compete to control a process in real-time. This work considers only the issues relating to implementation of the philosophy from a control theoretic perspective, issues relating to hardware/communications infrastructure and cyber security are beyond the scope of this work. A platform is formulated and all the relevant control requirements of the system are discussed. It is found that in order for such a platform to determine the behaviour of a controller, it would need to simulate the controller on a model of the process over an extended period of time. This would require a measure of the disturbance to be available, or at least an estimate thereof. This therefore increases the complexity of the platform. The practicality of implementing such a platform is discussed in terms of system identification and model/controller maintenance. A model of the surge tank from SibanyeStillwater’s Platinum bulk tailings treatment (BTT) plant, the aim of which is to keep the density of the tank outflow constant while maintaining a steady tank level, was derived, linearised and an input-output controllability analysis performed on the model. Six controllers were developed for the process, including four conventional feedback controllers (decentralised PI, inverse, modified inverse and H¥) and two Model Predictive Controllers (MPC) (one linear and another nonlinear). It was shown that both the inverse based and H¥ controllers fail to control the tank level to set-point in the event of an unmeasured disturbance. The competing concept was successfully illustrated on this process with the linear MPC controller being the most often selected controller, and the overall performance of the plant substantially improved by having access to more advanced control techniques, which is facilitated by the proposed platform. A first appendix presents an investigation into a previously proposed switching philosophy [15] in terms of its ability to determine the best controller, as well as the stability of the switching scheme. It is found that this philosophy cannot provide an accurate measure of controller performance owing to the use of one step ahead predictions to analyse controller behaviour. Owing to this, the philosophy can select an unstable controller when there is a stable, well tuned controller competing to control the process. A second appendix shows that there are cases where overall system performance can be improved through the use of the proposed platform. In the presence of constraints on the rate of change of the inputs, a more aggressive controller is shown to be selected so long as the disturbance or reference changes do not cause the controller to violate these input constraints. This means that switching back to a less aggressive controller is necessary in the event that the controller attempts to violate these constraints. This is demonstrated on a simple first order plant as well as the surge tank process. Overall it is concluded that, while there are practical issues surrounding plant and system identification and model/controller maintenance, it would be possible to implement such a platform which would allow a given plant access to advanced process control solutions without the need for procuring the services of a large vendor.
Dissertation (MEng)--University of Pretoria, 2020.
Electrical, Electronic and Computer Engineering
MEng
Unrestricted

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Pitta, Renato Neves. „Aplicação industrial de re-identificação de modelos de MPC em malha fechada“. Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-10042012-115001/.

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A identificação de modelos é usualmente a tarefa mais significativa e demorada no trabalho de implementação e manutenção de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC) tendo em vista a complexidade da tarefa e a importância que o modelo possui para um bom desempenho do controlador. Após a implementação, o controlador tende a permanecer com o modelo original mesmo que mudanças de processo tenham ocorrido levando a uma degradação das ações do controlador. Este trabalho apresenta uma aplicação industrial de re-identificação em malha fechada. A metodologia de excitação da planta utilizada foi apresentada em Sotomayor et al. (2009). Tal técnica permite obter o comportamento das variáveis de processo sem desligar o MPC e sem modificar sua estrutura, aumentando assim, o automatismo e a segurança do procedimento de re-identificação. O sistema re-identificado foi uma coluna debutanizadora de uma refinaria brasileira sendo que os modelos fazem parte do controle preditivo multivariável dessa coluna de destilação. A metodologia foi aplicada com sucesso podendo-se obter os seis novos modelos para atualizar o controlador em questão, o que resultou em uma melhoria de seu desempenho.
Model identification is usually the most significant and time-consuming task of implementing and maintaining control systems based on models (MPC) concerning the complexity of the task and the importance of the model for a good performance of the controller. After being implemented the MPC tends to remain with the original model even after process changes have occurred, leading to a degradation of the controller actions. The present work shows an industrial application of closed-loop re-identification. The plant excitation methodology used here was presented in Sotomayor et al. (2009). Such technique allows for obtaining the behavior of the process variables with the MPC still working and without modifying the MPC structure, increasing automation and safety of the re-identification procedure. The system re-identified was a debutanizer column of a Brazilian refinery being the models part of the multivariable predictive control of this distillation column. The methodology was applied with reasonable success managing to obtain 6 new models to update this MPC, and resulting in improved control performance.

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Jansson, Lovisa, und Amanda Nilsson. „Evaluation of Model-Based Design Using Rapid Control Prototyping on Forklifts“. Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158715.

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The purpose of this thesis is to evaluate Rapid Control Prototyping which is apart of the Model-Based Design concept that makes it possible to convenientlytest prototype control algorithms directly on the real system. The evaluation ishere done by designing two different controllers, a gain-scheduled P controllerand a linear Model Predictive Controller (mpc), for the lowering of the forks of aforklift.The two controllers are first tested in a simulation environment. The thesis con-tains two different simulation models: one physical where only minor parameteradjustments are done and one estimated black-box model. After evaluating thecontrollers in a simulation environment they are tested on a real forklift with areal-time target machine.The designed controllers have different strengths and weaknesses as one is non-linear and single variable, the P controller, and the other linear and multivariable,thempc. The P controller has a smooth movement in all situations without be-ing slow, unlike thempc. The disadvantage of the P controller compared to thempcis that there is no guarantee that the P controller will keep the speed limit,whereas thempcapproach gives such a guarantee.The better performance of the P controller outweighs the speed limit guaranteeand thus a conclusion is drawn that the nonlinearities of the system has a largereffect than the multivariable aspect. Also, another conclusion drawn is that work-ing with Model-Based Design and Rapid Control Prototyping makes it possibleto test many different ideas on a real forklift without spending a lot of time onimplementation.
Syftet med detta examensarbete är att utvärdera Rapid Control Prototyping vil-ket är en del av modellbaserad utveckling som gör det möjligt att enkelt testamodeller av styralgoritmer direkt på det riktiga systemet. Utvärderingen är gjordgenom att testa två olika regulatorer, en P-regulator med parameterstyrning ochen linjär modelbaserad prediktionsregulator (mpc), för sänkningen av gafflarnapå en truck.De två regulatorerna testas först i en simuleringsmiljö. I arbetet används två olikasimuleringsmodeller: en fysikalisk där endast mindre parameterjusteringar görsoch en estimerad black-box modell. Efter att regulatorerna utvärderas i simule-ringsmiljön testas de även på en riktig truck med hjälp av automatisk kodgenere-ring och exekvering på en dedikerad hårdvaruplattform.De konstruerade regulatorerna har olika för- och nackdelar eftersom en är olinjäroch envariabel, P-regulatorn, och en är linjär men flervariabel,mpc:n. P-regulatornhar en mjuk rörelse i alla lägen utan att bli för långsam, till skillnad frånmpc:n.Nackdelen med P-regulatorn, jämfört medmpc:n är att det inte finns någon ga-ranti för att P-regulatorn håller hastighetsbegränsningen sommpc:n gör.P-regulatorns bättre prestanda överväger garantin om att hålla hastighetsbegräns-ningen och därför dras slutsatsen att olinjäriteterna i systemet överväger effekter-na av det faktum att det också är flervariabelt. En annan slutsats är att modell-baserad utveckling och Rapid Control Prototyping gör det möjligt att testa fleraolika idéer på en riktig gaffeltruck utan att spendera för mycket tid på implemen-tationen.

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Cruz, Diego Déda Gonçalves Brito. „Detecção de erros planta-modelo em sistemas de controle preditivo (MPC) utilizando técnicas de informação mútua“. Universidade Federal de Sergipe, 2017. https://ri.ufs.br/handle/riufs/5028.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Model predictive control (MPC) strategies have become the standard for advanced control applications in the process industry. Significant benefits are generated from the MPC's capacity to ensure that the plant operates within its constraints more profitably. However, like any controller, after some time under operation, MPCs rarely function as when they were initially designed. A large percentage of performance degradation of MPC is associated with the deterioration of model that controller uses to predict process outputs and calculate inputs. The objective of the present work is implementation of mathematical methods that can be used to detect model-plant mismatch in linear and nonlinear MPC systems. In this work, techniques based on cross correlation, partial correlation and mutual information are implemented and tested by numerical simulation in case studies characteristic of the petrochemical industry, represented by linear and nonlinear models, operating under MPC control. The results obtained through the applying the techniques are analyzed and compared as to their efficiency is not intended to offer their potential for real industrial applications.
Estratégias de controle preditivo (MPC) têm-se tornado o padrão para aplicações de controle avançado na indústria de processos. Os benefícios significativos são gerados a partir da habilidade do controlador MPC de assegurar que a planta opere dentro das restrições de forma mais lucrativa. Porém, como todo controlador, depois de algum tempo em operação, os MPCs raramente funcionam como quando foram inicialmente projetados. Uma grande porcentagem da degradação do desempenho dos controladores MPC está associada à deterioração do modelo que o controlador usa para fazer a predição das saídas do processo e calcular as entradas. O objetivo do presente trabalho é a implementação de métodos matemáticos que possam ser utilizados para a detecção de erros planta-modelo em sistemas de controle MPC lineares e não lineares. Neste trabalho, técnicas baseadas em correlação cruzada, correlação parcial e informação mútua são implementadas e testadas por simulação numérica em estudos de caso característicos da indústria petroquímica, representados por modelos lineares e não lineares, operando sob controle MPC. Os resultados obtidos através da aplicação das técnicas são analisados e comparados quanto à sua eficiência no objetivo proposto avaliando seu potencial para aplicações industriais reais.

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Silva, Guilherme Moura Afonso da. „Reconciliação dinâmica de dados baseada em estimadores em uma malha de controle MPC“. Universidade Federal de Sergipe, 2017. https://ri.ufs.br/handle/riufs/5026.

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The data reconciliation in process control is extremely important regarding the industries because from this it is possible to obtain a greater efficiency in the performance in industrial process control meshes aiming at a lower cost and a higher quality of the product. In this work we approach data estimation techniques for the implementation of an online dynamic data reconciliation system in order to reduce the noise and the measurement uncertainties that are submitted in the process variables. The techniques used here are: the Kalman Filter, the Preditor-Corrector DDR Algorithm, the Moving Horizon Estimator (MHE) and the Constrained Extended Kalman Filter (CEKF). The analysis is performed by applying the dynamic data reconciliation system in a simulated process, characteristic of the chemical industry, operating under MPC (Model Predictive Control). The performance of the MPC controller is also enhanced by the use of the reconciled data in the feedback control loop.
A reconciliação de dados em controle de processos é extremamente importante no que diz respeito às indústrias, pois a partir dessa é possível obter uma maior eficiência no desempenho em malhas de controle de processos industriais visando à minimização dos custos e maximizando a qualidade do produto. Neste trabalho abordam-se técnicas de estimação de dados para a implementação de um sistema de reconciliação dinâmica de dados on-line a fim de reduzir os ruídos e as incertezas de medições a que estão submetidas às variáveis do processo. As técnicas aqui empregadas são: o Filtro de Kalman, o Algoritmo DDR Preditor-Corretor, o Estimador de Horizonte Móvel (MHE) e o Filtro de Kalman Estendido com Restrições (CEKF). As análises são efetuadas aplicando o sistema de reconciliação dinâmica de dados em um processo simulado, característico da indústria química, operando sob controle preditivo (MPC). Também é efetuado o aprimoramento no desempenho do controlador MPC utilizando os dados reconciliados na malha de realimentação do controlador.

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Fontes, Nayanne Maria Garcia Rego. „Monitoramento e avaliação de desempenho de sistemas MPC utilizando métodos estatísticos multivariados“. Universidade Federal de Sergipe, 2017. http://ri.ufs.br:8080/xmlui/handle/123456789/5037.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Monitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control.
O monitoramento de sistemas de controle de processos é extremamente importante no que diz respeito às indústrias, para garantir a qualidade do que é produzido e a segurança do processo. Os controladores preditivos, também conhecidos pela sigla em inglês MPC (Model Predictive Control), costumam ter um bom desempenho inicialmente. Entretanto, após um certo período, muitos fatores contribuem para a deterioração de seu desempenho. Isto evidencia a importância do monitoramento dos sistemas de controle MPC. Neste trabalho aborda-se ferramentas, baseada em métodos estatísticos multivariados, aplicados ao problema de monitoramento e avaliação de desempenho de controladores preditivos. Os métodos aqui apresentados são: o PCA (Análise por componentes principais) e o ICA (Análise por componentes independentes). Ambas são técnicas que utilizam dados coletados diretamente do processo. O primeiro é largamente utilizado na avaliação de desempenho de controladores preditivos. Já o segundo, é uma técnica mais recente que surgiu, principalmente, com o intuito de ser utilizado em sistemas de detecção de falhas. As análises são feitas quando aplicadas em processos simulados característicos da indústria petroquímica operando sob controle MPC.

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Mörhed, Joakim, und Filip Östman. „Automatic Parking and Path Following Control for a Heavy-Duty Vehicle“. Thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-144496.

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The interest in autonomous vehicles has never been higher and there are several components that need to function for a vehicle to be fully autonomous; one of which is the ability to perform a parking at the end of a mission. The objective of this thesis work is to develop and implement an automatic parking system (APS) for a heavy-duty vehicle (HDV). A delimitation in this thesis work is that the parking lot has a known structure and the HDV is a truck without any trailer and access to more computational power and sensors than today's commercial trucks. An automatic system for searching the parking lot has been developed which updates an occupancy grid map (OGM) based on measurements from GPS and LIDAR sensors mounted on the truck. Based on the OGM and the known structure of the parking lot, the state of the parking spots is determined and a path can be computed between the current and desired position. Based on a kinematic model of the HDV, a gain-scheduled linear quadratic (LQ) controller with feedforward action is developed. The controller's objective is to stabilize the lateral error dynamics of the system around a precomputed path. The LQ controller explicitly takes into account that there exist an input delay in the system. Due to minor complications with the precomputed path the LQ controller causes the steering wheel turn too rapidly which makes the backup driver nervous. To limit these rapid changes of the steering wheel a controller based on model predictive control (MPC) is developed with the goal of making the steering wheel behave more human-like. A constraint for maximum allowed changes of the controller output is added to the MPC formulation as well as physical restrictions and the resulting MPC controller is smoother and more human-like, but due to computational limitations the controller turns out less effective than desired. Development and testing of the two controllers are evaluated in three different environments of varying complexity; the simplest simulation environment contains a basic vehicle model and serves as a proof of concept environment, the second simulation environment uses a more realistic vehicle model and finally the controllers are evaluated on a full-scale HDV. Finally, system tests of the APS are performed and the HDV successfully parks with the LQ controller as well as the MPC controller. The concept of a self-parking HDV has been demonstrated even though more tuning and development needs to be done before the proposed APS can be used in a commercial HDV.

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André, Simon. „Design and Optimization of Controllers for an Electro-Hydraulic System“. Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-107620.

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Electro-Hydraulic (EH) systems are commonly used in the industry for applications that require high power-weight ratios and large driving forces. The EH system studied in this master thesis have recently been upgraded with new hardware components and as a part of this upgrade a new controller was requested. The system consists of a controller that computes a control signal for an electric motor. The motor drives a gear pump that generates a flow of hydraulic fluid. The flow is then directed to a cylinder. The movements of a piston in the cylinder is affected by the flow and the piston position can be measured. The measured piston position is then fed back to the controller and the control loop is complete. The system was previously controlled using a Proportional-Integral-Derivative (PID) controller and the purpose of this thesis is to compare the old controller with alternative control strategies suitable for this application. The evaluation of the controllers is based on both software and hardware simulations and results in a recommendation for final implementation of the best suited controller. The control strategies chosen for investigation are: a retuned PID controller, a PID controller with feed forward from reference, a PID based cascade controller, a Linear Quadratic (LQ) controller, and a Model Predictive Controller (MPC). To synthesize the controllers an approximate model of the system is formed and implemented in the software environment Matlab Simulink. The model is tuned to fit recorded data and provides a decent estimation of the actual system. The proposed control strategies are then simulated and evaluated in Simulink with the model posing as the real system. These simulations resulted in the elimination of the cascade controller as a possible candidate since it proved unstable for large steps in the reference signal. The remaining four controllers were all selected for simulation on the real hardware system. Unfortunately the MPC was never successfully implemented on the hardware due to some unknown compatibility error and hence eliminated as a possible candidate. The three remaining control strategies, PID, PID with feed forward from reference and the LQ controller, were all successfully implemented and simulated on hardware. The results from the hardware simulations compared to simulations made with the old controller, as well as the results from the software simulations, were then evaluated. Depending on the purpose one of two control strategies is recommended for this application. The LQ controller achieved the best overall performance and is presented as the control strategy best suited for this application.

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Ng, Desmond Han Tien. „Stochastic model predictive control“. Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:b56df5ea-10ee-428f-aeb9-1479ce9a7b5f.

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The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) algorithm for linear systems with additive and multiplicative stochastic uncertainty subjected to linear input/state constraints. Constraints can be in the form of hard constraints, which must be satisfied at all times, or soft constraints, which can be violated up to a pre-defined limit on the frequency of violation or the expected number of violations in a given period. When constraints are included in the SMPC algorithm, the difficulty arising from stochastic model parameters manifests itself in the online optimization in two ways. Namely, the difficulty lies in predicting the probability distribution of future states and imposing constraints on closed loop responses through constraints on predictions. This problem is overcome through the introduction of layered tubes around a centre trajectory. These tubes are optimized online in order to produce a systematic and less conservative approach of handling constraints. The layered tubes centered around a nominal trajectory achieve soft constraint satisfaction through the imposition of constraints on the probabilities of one-step-ahead transition of the predicted state between the layered tubes and constraints on the probability of one-step-ahead constraint violations. An application in the field of Sustainable Development policy is used as an example. With some adaptation, the algorithm is extended the case where the uncertainty is not identically and independently distributed. Also, by including linearization errors, it is extended to non-linear systems with additive uncertainty.

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Barsk, Karl-Johan. „Model Predictive Control of a Tricopter“. Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79066.

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In this master thesis, a real-time control system that stabilizes the rotational rates of a tri-copter, has been studied. The tricopter is a rotorcraft with three rotors. The tricopter has been modelled and identified, using system identification algorithms. The model has been used in a Kalman filter to estimate the state of the system and for design ofa model based controller. The control approach used in this thesis is a model predictive controller, which is a multi-variable controller that uses a quadratic optimization problem to compute the optimal con-trol signal. The problem is solved subject to a linear model of the system and the physicallimitations of the system. Two different types of algorithms that solves the MPC problem have been studied. These are explicit MPC and the fast gradient method. Explicit MPC is a pre-computed solution to the problem, while the fast gradient method is an online solution. The algorithms have been simulated with the Kalman filter and were implemented on themicrocontroller of the tricopter.

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Elgharib, Ahmed Omar Ahmed. „Différentes stratégies de contrôle pour le système d'éolienne connecté PMSG“. Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0647.

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L'énergie éolienne est l'une des sources d'énergie renouvelables les plus attrayantes et prometteuses. Celle-ci offre un excellent substitut à la production d'énergie électrique traditionnelle. Les éoliennes basées sur un PMSG sont les mieux adaptées aux applications autonomes en raison de leur fiabilité et de leur haute efficacité. L'énergie éolienne a continué à jouer un rôle important et peut être considérée comme la source d'énergie renouvelable la plus déployée. Ce travail de recherche propose quelques méthodes de contrôle efficaces associées au contrôle de l'énergie éolienne. Il porte principalement sur le réajustement de certaines approches de contrôle disponibles, comme l'amélioration du NSSFC et du NDSFC, afin d'augmenter les performances du contrôleur pour un tel système. En parallèle, ce travail traite le contrôleur NPIC qui a été ajouté au système en présentant une technique de contrôle sans capteur d'une éolienne PMSG à entraînement direct. Ensuite, le contrôleur PI est étudié dans ce travail en intégrant un algorithme génétique qui a un impact significatif sur l'efficacité et l'exécution des applications éoliennes et de leur système entier. Le MPC est le dernier contrôleur qui a été exploré avec ses résultats de simulation pour le système. Tous ces contrôleurs utilisent PMSG. Plusieurs tests expérimentaux ont été appliqués à une grande variété de configurations, afin de valider les résultats de simulation obtenus. Cette thèse de recherche servira comme référence pour les études futures sur le contrôle des systèmes d'éoliennes
Renewable energy is considered as a viable alternative to conventional fossil fuel generators globally. One of the appealing and promising renewable energy sources is wind energy. This renewable energy source offers an excellent substitute for the generation of traditional electricity. Wind turbines based on PMSG are best suited for stand-alone applications due to their reliability. This research work proposes some efficient control methods associated with wind energy control. It is focused more on the readjustment of some available control approaches as the improvement of NSSFC (nonlinear static state feedback controller) and NDSFC (nonlinear dynamic state feedback controller) to increase the controller performance for such a system. In sequence with that, this work moves forward to another controller(NPIC) which has been added to this system by presenting a sensor-less control technique of direct driven PMSG wind turbine. Afterwards, PI Controller is studied in this work by integrating genetic algorithm that has significant impact on the efficiency and execution of wind turbine applications and their whole system. Model predictive control (MPC) is thelast controller that has been explored. All of these controllers are using PMSG, discussed under different operating ranges of wind speed. Several experimental tests were applied to wide variety of configurations in order to validate the simulation results produced. This research aims to serve as a detailed reference for future studies on the control of wind turbine systems

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Asar, Isik. „Model Predictive Control (mpc) Performance For Controlling Reaction Systems“. Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605001/index.pdf.

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In this study, the performance of the Model Predictive Controller (MPC) algorithm is investigated in two different reaction systems. The first case is a saponification reaction system where ethyl acetate reacts with sodium hydroxide to produce sodium acetate and ethanol in a CSTR. In the reactor, temperature and sodium acetate concentration are controlled by manipulating the flow rates of ethyl acetate and cooling water. The model of the reactor is developed considering first principal models. The experiments are done to obtain steady state data from the reaction system and these are compared with the model outputs to find the unknown parameters of the model. Then, the developed model is used for designing SISO and MIMO-MPC considering Singular Value Decomposition (SVD) technique for coupling. The second case is the reaction system used for the production of boric acid by the reaction of colemanite and sulfuric acid in four CSTR&rsquo
s connected in series. In the reactor, the boric acid concentration in the fourth reactor is controlled by manipulating the sulfuric acid flow rate fed to the reactor. The transfer functions of the process and disturbance (colemanite flow rate) are obtained experimentally by giving step changes to the manipulated variable and to the disturbance. A model-based and constrained SISO-MPC is designed utilizing linear step response coefficients. The designed controllers are tested for performance in set point tracking, disturbance rejection and robustness issues for the two case studies. It is found that, they are satisfactory except in robustness issues for disturbance rejection in boric acid system.

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Morinelly, Sanchez Juan Eduardo. „Adaptive Model Predictive Control with Generalized Orthonormal Basis Functions“. Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1091.

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An adaptive model predictive control (MPC) method using models derived from orthonormal basis functions is presented. The defining predictor dynamics are obtained from state-space realizations of finite truncations of generalized orthonormal basis functions (GOBF). A structured approach to define multivariable system models with customizable, open-loop stable linear dynamics is presented in Chapter 2. Properties of these model objects that are relevant to the adaptation component of the overall scheme, are also discussed. In Chapter 3, non-adaptive model predictive control policies are presented with the definition of extended state representations through filter operations that enable output feedback. An infinite horizon set-point tracking policy which always exists under the adopted modeling framework is presented. This policy and its associated cost are included as the terminal stage elements for a more general constrained case. The analysis of robust stability guarantees for the non-adaptive constrained formulation is presented, under the assumption of small prediction errors. In Chapter 4, adaptation is introduced and the certainty equivalence constrained MPC policy is formulated under the same framework of its non-adaptive counterpart. Information constraints that induce the excitation of the signals relevant to the adaptation process are formulated in Chapter 5. The constraint generation leverages the GOBF model structure by enforcing a sufficient richness condition directly on the state elements relevant to the control task. This is accomplished by the definition of a selection procedure that takes into account the characteristics of the most current parameter estimate distribution. Throughout the manuscript, illustrative simulation examples are provided with respect to minimal plant models. Concluding remarks and general descriptions for potential future work are outlined in Chapter 6.

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Greer, William Bryce. „Advanced Linear Model Predictive Control For Helicopter Shipboard Maneuvers“. Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95031.

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This dissertation focuses on implementing and analyzing advanced methods of model predictive control to control helicopters into stable flight near a ship and perform a soft touchdown from that state. A shrinking horizon model predictive control method is presented which can target specific states at specific times and take into account several important factors during landing. This controller is then used in simulation to perform a touchdown maneuver on a ship for a helicopter by targeting a landed state at a specific time. Increasing levels of fidelity are considered in the simulations. Computational power required reduces the closer the helicopter starts to the landing pad. An infinite horizon model predictive controller which allows simultaneous cost on state tracking, control energy, and control rates and allows tracking of an arbitrary equilibrium to infinity is then presented. It is applied in simulation to control a helicopter initially in a random flight condition far from a ship to slowly transition to stable flight near the ship, holding an arbitrary rough position relative to the ship indefinitely at the end. Three different target positions are simulated. This infinite horizon control method can be used to prepare for landing procedures that desire starting with the helicopter in some specific position in close proximity to the landing pad, such as the finite horizon method of landing control described previously which should start with the helicopter close to the ship to reduce computation power required. A method of constructing a landing envelope is then presented and used to construct a landing envelope for the finite horizon landing controller. A pre-existing method of combining linear controllers to account for nonlinearity is then slightly modified and used on implementations of the finite horizon landing controller to make a control that takes into account some of the nonlinearity of the problem. This control is tested in simulation.
Doctor of Philosophy
This dissertation proposes and, using simulation, analyzes control algorithms and their use on helicopter shipboard operations. Various benefits and advances for controls in this area are suggested, tested, and discussed. The control methods presented and implemented, while not limited to these use cases, are particularly well suited for them. One control algorithm is used for controlling flight near the landing point on a ship and performing a soft touchdown on the ship. The algorithm is tested in simulation. Another algorithm is used to control a helicopter initially in flight far away from the ship to slowly transition to stable flight near the ship, holding a rough position relative to the ship indefinitely at the end. This control could be used to set up the helicopter for later use of the touchdown control. This control is also tested in simulation. A method of quantifying what conditions the touchdown controller has a relatively good chance of successfully landing in is then suggested. The range of conditions for which successful touchdown has a relatively high chance of being achieved along with an analysis of that likelihood is called the landing envelope. Using the landing envelope construction method with numerous simulations, a landing envelope for the touchdown controller is obtained. The touchdown controller assumes that the helicopter’s dynamics are linear. Helicopter dynamics (like most dynamics of real systems) are nonlinear. However, under conditions near the point that dynamics are linearized about, a linear approximation is sufficiently accurate. To improve on the above landing algorithm, a method of combining multiple specific implementations of the touchdown controller to help account for nonlinearity to improve the approximation of the dynamics that the controller assumes is then suggested and performed in simulation.

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Andre, do Nascimento Allan. „Robust Model Predictive Control for Marine Vessels“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247883.

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This master thesis studies the implementation of a Robust MPC controllerin marine vessels on different tasks. A tube based MPC is designed based onsystem linearization around the target point guaranteeing local input to statestability of the respective linearized version of the original nonlinear system.The method is then applied to three different tasks: Dynamic positioningon which recursive feasibility of the nominal MPC is also guaranteed, Speed-Heading control and trajectory tracking with the Line of sight algorithm.Numerical simulation is then provided to show technique’s effectiveness.
Detta examensarbete studerar design och implementering av en robustmodellprediktiv regulator (MPC) för marina fartyg. En tub-baserad MPCär designad baserad på linjärisering av systemdynamiken runt en målpunkt,vilket garanterar local insignal-till-tillstånds stabilitet av det linjäriserade systemet.Metoden är sedan applicerad på tre olika uppgifter: dynamisk positionering,för vilken vi även kan garantera rekursiv lösbarhet för den nominellaregulatorn; riktningsstyrning; och banfötljning med en siktlinje-algoritm. Numeriskasimuleringsstudier bekräftar metodens effektivitet.

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Kestner, Brian. „Model predictive control (MPC) algorithm for tip-jet reaction drive systems“. Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31802.

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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Mavris, Dimitri; Committee Member: German, Brian; Committee Member: Healy, Tim; Committee Member: Rosson, Randy; Committee Member: Tai, Jimmy. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Elliott, Matthew Stuart. „Decentralized model predictive control of a multiple evaporator HVAC system“. [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3001.

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Friedbaum, Jesse Robert. „Model Predictive Linear Control with Successive Linearization“. BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7063.

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Robots have been a revolutionizing force in manufacturing in the 20th and 21st century but have proven too dangerous around humans to be used in many other fields including medicine. We describe a new control algorithm for robots developed by the Brigham Young University Robotics and Dynamics and Robotics Laboratory that has shown potential to make robots less dangerous to humans and suitable to work in more applications. We analyze the computational complexity of this algorithm and find that it could be a feasible control for even the most complicated robots. We also show conditions for a system which guarantee local stability for this control algorithm.

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Kemppainen, Josefin. „Model Predictive Control for Heavy Duty Vehicle Platooning“. Thesis, Linköpings universitet, Institutionen för systemteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78963.

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The aim of platooning is to enable several vehicles to drive in a convoy while each vehicle is controlled autonomously in longitudinal direction. All vehicles in the platoon are equipped with WiFi and can therefore apply Vehicle-to-Vehicle (V2V) communication. As a result, a short intermediate distance between the vehicles can be maintained. Reduction of the aerodynamic drag is the result of the short distance, which in turn, reduces the consumed fuel. This thesis is a part of a larger project, consisting of two other theses that investigate estimation of the sensor data. Other scenarios that may arise with the platooning concept, e.g. packet losses and time synchronization of the different sensors are also analyzed. The purpose of this master thesis is to develop and evaluate a Model Predictive Control (MPC) in the concept of platooning. The main focus lies on implementation of two types of MPC, centralized and distributed, and later on integration with the other two subsystems is performed. Results from the MPC itself are evaluated, principally in terms of fuel con- sumption and computational demand. The major part of the results are based on the complete system as one unit and covers different test scenarios such as WiFi loss and non-transmitting vehicle entering the platoon. A comparison of how much energy that is consumed by the engine between an HDV driving with its cruise control and an HDV driving in a platoon has been performed. With an intermediate distance of 10 meters, driving with varying velocity and ideal signals the energy consumption got reduced with an average of 11%.
Syftet med platooning är att flera tunga fordon kör tätt efter varandra i ett fordonståg. Varje fordon regleras autonomt i longitudinell riktning och är utrustad med WiFi. Detta bidrar till att fordonen kan kommunicera med varandra och denna kommunikation, även kallad Vehicle-to-Vehicle (V2V) - communication, leder till att det relativa avståndet mellan fordonen kan minskas, vilket i sin tur leder till minskat luftmotstånd och därmed minskad bränsleförbrukning. Detta examensarbete är en del av ett större projekt som består av ytterligare två examensarbeten. De andra två hanterar estimeringen av sensordata samt behandlar förlorat sensordata och tidssynkronisering av de olika sensorerna som används. Syftet med detta examensarbete är att utveckla och utvärdera en MPC regu- lator i platooning sammanhang. Huvudfokuset ligger på implementeringen, både centraliserad och distribuerad MPC, och integreringen med de två andra delsystemen. Resultaten från enbart MPC utvärderas i termer av bränsleförbrukning och även beräkningskapactiet, då MPC är känt för att vara väldigt beräkningskrävan- de och är ofta en begränsning för hårdvaran. Den största delen av resultaten är baserade på hela systemet och täcker olika scenarion som exempelvis dålig WiFi uppkoppling och att icke−sändande fordon intar platoonen. En jämförelse av hur mycket energi motorn förbrukade har gjorts mellan ett tungt fordon som kör med farthållaren påslagen och ett tungt fordon som kör i en platoon. Med ett relativt avstånd på 10 meter, varierande hastighet och icke brusiga signaler kan bränsleförbrukning minskas med ett medel på approximativt 11%.

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Norstedt, Erik, und Olof Bräne. „Model Predictive Climate Control for Electric Vehicles“. Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446435.

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This thesis explores the possibility of using an optimal control scheme called Model Predictive Control (MPC), to control climatization systems for electric vehicles. Some components of electric vehicles, for example the batteries and power electronics, are sensitive to temperature and for this reason it is important that their temperature is well regulated. Furthermore, like all vehicles, the cab also needs to be heated and cooled. One of the weaknesses of electric vehicles is their range, for this reason it is important that the temperature control is energy efficient. Once the range of electric vehicles is increased the down sides compared to traditional combustion engine vehicles decrease, which could lead to an increase in the usage of electric vehicles. This could in turn lead to a decrease of greenhouse gas emission in the transportation sector. With the help of MPC it is possible for the controller to take more factors into consideration when controlling the system than just temperature and in this thesis the power consumption and noise are also taken into consideration. A simple model where parts of the climate system’s circuits were seen as point masses was developed, with nonlinear heat transfers occurring between them, which in turn were controlled by actuators such as fans, pumps and valves. The model was created using Simulink and MATLAB, and the MPC toolbox was used to develop nonlinear MPC controllers to control the climate system. A standard nonlinear MPC, a nonlinear MPC with custom cost functions and a PI controller where all developed and compared in simulations of a cooling scenario. The controllers were designed to control the temperatures of the battery, power electronics and the cab of an electric vehicle. The results of the thesis indicate that MPC could reduce power consumption for the climate control system, it was however not possible to draw any final conclusions as the PI controller that the MPC controllers were compared to was not well optimized for the system. The MPC controllers could benefit from further work, most importantly by applying a more sophisticated tuning method to the controller weights. What was certain was that it is possible to apply this type of centralized controller to very complex systems and achieve robustness without external logic. Even with the controller keeping track of six different temperatures and controlling 15 actuators, the control loop runs much faster than real time on a modern computer which shows promise with regard to implementing it on an embedded system.

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Sha'Aban, Yusuf. „Regulatory level model predictive control“. Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/regulatory-level-model-predictive-control(1cca6fc1-8473-4191-8edd-06ddb0884040).html.

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The need to save energy, cut costs, and increase profit margin in process manufactureincreases continually. There is also a global drive to reduce energy use and cut down co2 emission and combat climate change. These in turn have led to more stringent requirements on process control performance. Hence, the requirements for modern systems are often not achievable using classical control techniques. Therefore, advanced control strategies are often required to ensure optimal process performance. Despite these challenges, PID has continued to be the dominant industrial control scheme. However, for systems with complex dynamics and/or high performance requirements, PID control may not be sufficient. Therefore, a significant number of industrial control loops are not performing optimally and more advanced control than PID may be required in order to achieve optimal performance. MPC is one of the advanced control schemes that has had a significant impact in the industry. Despite the benefits associated with the implementation of MPC, the technology has remained a niche application in process manufacture. This thesis seeks to address these issues by developing ways that could lead to widespread application of MPC. In the first part of this thesis, a study was carried out to understand the characteristics of processes that would benefit from the application of MPC at the regulatory control level even in the single-input single-output (SISO) case. This is a departure from the common practice in which MPC is applied at the supervisory control layer delivering set points to PID controllers at the regulatory control layer. Both numerical simulation and industrial studies were used to show and quantify benefits of MPC for SISO applications at the regulatory control layer. Some issues that have led to the limited application of MPC include the cost and human efforts associated with modelling and controller design. And to achieve high process performance, accurate models are required. To address this issue, in the second part of this thesis, a novel technique for designing MPC from routine plant data – routine data MPC (RMPC) is proposed. The proposed technique was successfully implemented on process models. This technique would reduce the high human cost associated with MPC deployment, which could make it a widespread rather than niche application in the process manufacturing industry.

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Sundbrandt, Markus. „Control of a Ground Source Heat Pump using Hybrid Model Predictive Control“. Thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71369.

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The thesis has been conducted at Bosch Thermoteknik AB and its aim is to develop a Model Predictive Control (MPC) controller for a ground source heat pump which minimizes the power consumption while being able to keep the inside air temperature and Domestic Hot Water (DHW) temperature within certain comfortintervals. First a model of the system is derived, since the system consists of both continuous and binary states a hybrid model is used. The MPC controller utilizes the model to predict the future states of the system, and by formulating an optimizationproblem an optimal control is achieved. The MPC controller is evaluated and compared to a conventional controller using simulations. After some tuning the MPC controller is capable of maintaining the inside air and DHW temperature at their reference levels without oscillating too much. The MPC controller’s general performance is quite similar to the conventional controller, but with a power consumption which is 1-3 % lower. A simulation using an inside air temperature reference which is lowered during the night is also conducted, it achieved a power consumption which was 7.5 % lower compared to a conventional controller.

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Simon, Daniel. „Fighter Aircraft Maneuver Limiting Using MPC : Theory and Application“. Doctoral thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139945.

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Flight control design for modern fighter aircraft is a challenging task. Aircraft are dynamical systems, which naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems is becoming increasingly important as the performance and complexity of the aircraft is constantly increasing. The aeronautical industry has traditionally applied feedforward, anti-windup or similar techniques and different ad hoc engineering solutions to handle constraints on the aircraft. However these approaches often rely on engineering experience and insight rather than a theoretical foundation, and can often require a tremendous amount of time to tune. In this thesis we investigate model predictive control as an alternative design tool to handle the constraints that arises in the flight control design. We derive a simple reference tracking MPC algorithm for linear systems that build on the dual mode formulation with guaranteed stability and low complexity suitable for implementation in real time safety critical systems. To reduce the computational burden of nonlinear model predictive control we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefit of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence. An alternative to deriving MPC algorithms with guaranteed stability properties is to analyze the closed loop stability, post design. Here we focus on deriving a tool based on Mixed Integer Linear Programming for analysis of the closed loop stability and robust stability of linear systems controlled with MPC controllers. To test the performance of model predictive control for a real world example we design and implement a standard MPC controller in the development simulator for the JAS 39 Gripen aircraft at Saab Aeronautics. This part of the thesis focuses on practical and tuning aspects of designing MPC controllers for fighter aircraft. Finally we have compared the MPC design with an alternative approach to maneuver limiting using a command governor.

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Gustavsson, Andreas. „Dynamic modeling and Model Predictive Control of a vapor compression system“. Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76352.

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The focus of this thesis was on the development of a dynamic modeling capability for a vapor compression system along with the implementation of advanced multivariable control techniques on the resulting model. Individual component models for a typical vapor compression system were developed based on most recent and acknowledged publications within the field of thermodynamics. Parameter properties such as pressure, temperature, enthalpy etc. for each component were connected to detailed thermodynamic tables by algorithms programmed in MATLAB, thus creating a fully dynamic environment. The separate component models were then interconnected and an overall model for the complete system was implemented in SIMULINK. An advanced control technique known as Model Predictive Control (MPC) along with an open-source QP solver was then applied on the system. The MPC-controller requires the complete state information to be available for feedback and since this is often either very expensive (requires a great number of sensors) or at times even impossible (difficult to measure), a full-state observer was implemented. The MPC-controller was designed to keep certain system temperatures within tight bands while still being able to respond to varying cooling set-points. The control architecture was successful in achieving the control objective, i.e. it was shown to be adaptable in order to reflect changes in environmental conditions. Cooling demands were met and the temperatures were successfully kept within given boundaries.

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Lundh, Joachim. „Model Predictive Control for Active Magnetic Bearings“. Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81325.

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This thesis discuss the possibility to position control a rotor levitated with active magnetic bearings. The controller type considered is model predictive control which is an online strategy that solves an optimization problem in every sample, making the model predictive controller computation-intense. Since the sampling time must be short to capture the dynamics of the rotor, very little time is left for the controller to perform the optimization. Different quadratic programming strategies are investigated to see if the problem can be solved in realtime. Additionally, the impact of the choices of prediction horizon, control horizon and terminal cost is discussed. Simulations showing the characteristics of these choises are made and the result is shown.
Det här examensarbetet diskuterar möjligheten att positionsreglera en rotor som leviteras på aktiva magnetlager. Reglerstrategin som används är modellbaserad prediktionsreglering vilket är en online-metod där ett optimeringsproblem löses i varje sampel. Detta gör att regulatorn blir mycket beräkningskrävande. Samplingstiden för systemet är mycket kort för att fånga dynamiken hos rotorn. Det betyder att regulatorn inte ges mycket tid att lösa optimeringsproblemet. Olika metoder för att lösa QP-problem betraktas för att se om det är möjligt att köra regulatorn i realtid. Dessutom diskuteras hur valet av prediktionshorisont, reglerhorisont och straff på sluttillståndet påverkar regleringen. Simuleringar som visar karakteristiken av dessa val har utförts.

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Toschi, Alessandro. „Integration of Model Predictive Control for autonomous racing“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Autonomous driving is one of the technologies that could impact significantly society in the next decades. While various advanced driver assistance systems (ADAS) have already been introduced in commercial passenger vehicles, the technology for fully self- driving cars is not yet ready. The Indy Autonomous Challenge is a competition between universities and research centers, born to advance the technology in this field by com- peting in autonomous racecar events. The IAC seeks to increase public awareness of the transformational impact that automation can have on society and solve edge-case scenarios unlikely to happen in an urban scenario but with the need of be addressed to ensure safety. The focus of this thesis is on the integration of the controller, a model predictive control (MPC), used in two of these challenges. This class of control, based on a constrained op- timal control scheme, is usually used to cope with challenging situations and was suitable for handling an autonomous car at high speeds.

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Mancino, Francesco. „An embedded model predictive controller for optimal truck driving“. Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205649.

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An embedded model predictive controller for velocity control of trucks is developed and tested. By using a simple model of a heavy duty vehicle and knowledge about the slope of the road ahead, the fuel consumption while traveling near a set speed is diminished by almost 1% on an example road compared to a rule based speed control system. The problem is formulated as a look-ahead optimization problem were fuel consumption and total trip time have to be minimized. To find the optimal solution dynamic programming is used, and the whole code is designed to run on a Scania gearbox ECU in parallel with all the current software. Simulations were executed in a Simulink environment, and two test rides were performed on the E4 motorway.
En algoritm för hastighetsstyrning baserad på modell-prediktiv reglering har utvecklats och testats på befintlig styrsystem i ett Scania lastbil. Genom att använda en enkel modell av fordonet och kunskap om lutningen på vägen framför den kunde man sänka bränsleförbrukningen med nästan 1% i vissa sträckor, jämfört med en regelbaserad farthållare. Problemet är formulerat som en optimerings-problem där bränsleförbrukning och total restid måste minimeras. För att hitta den optimala lösningen användes dynamisk programmering och hela koden är skriven så att den kan exekveras på en Scania styrenehet. Koden är kan köras parallellt med den mjukvara som är installerad på styrenheten. Simuleringar utfördes i en miljö utvecklad i Simulink. Två test-körningar på E4 motorvägen utfördes.

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Carlsson, Rickard. „A practical approach to detection of plant model mismatch for MPC“. Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56581.

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The number of MPC installations in industry is growing as a reaction to demands of increased efficiency. An MPC controller uses an internal plant model to run real-time predictive optimization of future inputs. If a discrepancy between the internal plant model and the plant exists, control performance will be affected. As time from commissioning increases the model accuracy tends to deteriorate. This is natural as the plant changes over time. It is important to detect these changes and re-identify the plant model to maintain control performance over time. A method for identifying Model Plant Mismatch for MPC applications is developed. Focus has been on developing a method that is simple to implement but still robust. The method is able to run in parallel with the process in real time. The efficiency of the method is demonstrated via representative simulation examples.An extension to detection of nonlinear mismatch is also considered, which is important since linear plant models often are used within a small operating range. Since most processes are nonlinear this discrepancy is inevitable and should be detected.

Ökade krav på effektivitet gör att industrin söker efter mer avancerad processtyrning. MPC har växt fram som en kandidat. En MPC regulator änvänder en modell av systemet för att samtidigt som systemet körs utföra en optimering av framtida styrsignaler. Om modellen innehåller felaktigheter kan reglerprestandan påverkas. En modell försämras normalt då tiden från idrifttagning växer eftersom systemet förändras med tiden. Det är av största vikt att upptäcka dessa förändringar och sedan uppdatera modellen för att reglerprestandan inte ska påverkas. Avsikten är att utveckla en metod för att upptäcka modellfel med fokus på att den ska vara enkel att implementera. Det ska även vara möjligt att använda metoden parallellt med en process. För att utvärdera metoden så körs den på ett antal representativa simuleringsexempel. Det har även varit en avsikt att utveckla en metod för detektion av ickelinjära modellfel. Motivet till det är att linjära modeller används för att beskriva ickelinjära processer och då är modellfel naturliga.

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Hellström, Erik. „Explicit use of road topography for model predictive cruise control in heavy trucks“. Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2843.

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New and exciting possibilities in vehicle control are revealed by the consideration of topography through the combination GPS and three dimensional road maps. This thesis explores how information about future road slopes can be utilized in a heavy truck with the aim at reducing the fuel consumption over a route without increasing the total travel time.

A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function is used to define the optimization criterion. Through the function parameters the user is enabled to decide how fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use are weighed.

Computer simulations with a load of 40 metric tons shows that the fuel consumption can be reduced with 2.5% with a negligible change in travel time, going from Link¨oping to J¨onk¨oping and back. The road slopes are calculated by differentiation of authentic altitude measurements along this route. The complexity of the algorithm when achieving these results allows the simulations to run two to four times faster than real time on a standard PC, depending on the desired update frequency of the control signals.

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Juhlin-Henricson, Teddy. „Implementation and Analysis of a Clothoid-based Model Predictive Controller“. Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187688.

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For the last couple of years autonomous driving has increased in popularity as a research area, and it continues to grow. A topic within autonomous driving is path following, which is the subject studied in this project. One of the popular controllers to use for controlling a vehicle is the model predictive controller, because it finds an optimal control input for the vehicle based on the model of the vehicle, and its estimated future behaviour within the prediction horizon - which covers a distance ahead of the vehicle. To increase the length of this distance, one can use a new controller - the clothoid-based model predictive controller. The clothoid-based model predictive controller is a linear time-varying model predictive controller that uses a clothoid-based vehicle model to find an optimal input based on the vehicle’s behaviour at the kink-points. The kink-points are way-points that are used to create the clothoids, and the distance between them can be very far. Therefore, it is possible to cover a large distance ahead of the vehicle with a small prediction horizon. In this thesis, the controller is implemented at the Smart Mobility Laboratory at KTH Royal Institute of Technology so that it can be tested and evaluated for future use. The controller is implemented on a 1 : 32 scaled radio truck that is monitored by a motion capture system, and remotely controlled by a desktop computer. The outcome of the implementation is a new controller for the remote controlled radio trucks with a fast control algorithm, where the greatest mean deviation from the path was 0.117m.
Under de senaste åren har självkörande fordon blivit populärare som forskningsområde, och det blir allt mer populärt. Ett område inom självkörande fordon är att den ska följa efter en bana, även kallat path following, vilket är området som projektet fokuserat på. En av de populära kontrollerna för att styra fordonet är predikterande modell-kontroller (model predictive control), för den hittar en den optimala kontrol signalen baserat på modellen av fordonet och dess framtida bettende inom prediktions horisonten - som täcker ett område framför bilen. För att öka täckningsgraden av det här området kan en använda en ny kontroller - den klotoidbaserade predikterande modell-kontroller (clothoid-based model predictive controller). Den klotoidbaserade predikterande modell-kontroller är en linjärt tidsvarierande predikterande modell-kontroller (linear time-varying model predictive controller) som använder sig av en klotoidbaserad fordonsmodel för att hitta den optimala inputsignalen baserat på fordonets beteende vid knut-punkterna (kink-points). Knut-punkterna är punkter som används för att skapa klotoiderna, och avståndet mellan punkterna kan vara långt. Därför är det möjligt att täcka ett större område framför fordonet med en mindre prediktions horisont. I den här uppsatsen är kontroller implementerad i Smart Mobility Laboratory på Kungliga Tekniska Högskolan, så att den kan bli evaluerad och testad för användning i framtiden. Kontrollern används på˚ en 1 : 32 skalenlig radiostyd lastbild som är övervakad av ett rörelse detektionssystem, och lastbilen är radio fjärrstyrd via en dator. Resultatet av implementeringen är en ny kontroller för radiostyrd lastbil med en snabb kontroller algorithm med en maximal medelavvikelse från banan på 0.117m.

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Braghieri, Giovanni. „Application of robust nonlinear model predictive control to simulating the control behaviour of a racing driver“. Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275524.

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The work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory.

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Norén, Christoffer. „Path Planning for Autonomous Heavy Duty Vehicles using Nonlinear Model Predictive Control“. Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95547.

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In the future autonomous vehicles are expected to navigate independently and manage complex traffic situations. This thesis is one of two theses initiated with the aim of researching which methods could be used within the field of autonomous vehicles. The purpose of this thesis was to investigate how Model Predictive Control could be used in the field of autonomous vehicles. The tasks to generate a safe and economic path, to re-plan to avoid collisions with moving obstacles and to operate the vehicle have been studied. The algorithm created is set up as a hierarchical framework defined by a high and a low level planner. The objective of the high level planner is to generate the global route while the objectives of the low level planner are to operate the vehicle and to re-plan to avoid collisions. Optimal Control problems have been formulated in the high level planner for the use of path planning. Different objectives of the planning have been investigated e.g. the minimization of the traveled length between the start and the end point. Approximations of the static obstacles' forbidden areas have been made with circles. A Quadratic Programming framework has been set up in the low level planner to operate the vehicle to follow the high level pre-computed path and to locally re-plan the route to avoid collisions with moving obstacles. Four different strategies of collision avoidance have been implemented and investigated in a simulation environment.

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Sjögren, Sofia, und Nina Wollinger. „Slutfasstyrning av robot : en jämförelse mellan LQ och MPC“. Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10539.

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Arbetet har utförts på Saab Bofors Dynamics i Karlskoga och dess syfte var att undersöka om det är möjligt att använda modellbaserad prediktionsreglering, MPC, vid slutfasstyrning av en viss typ av robot. Som referensram används linjärkvadratisk reglering, LQ, eftersom denna reglermetod har undersökts tidigare och visat sig fungera bra vid slutfasstyrning, dock för en annan typ av robot. Anledningen till att man vill undersöka om det är möjligt att använda MPC är att styrlagen enkelt tar hand om begränsningar på systemet på ett direkt och intuitivt sätt.

Styrlagarnas uppgift är att styra en robot i dess slutfas då det finns krav och önskemål på roboten som bör vara uppfyllda. Till exempel finns det begränsningar på styrsignalen samt önskemål om att träff ska ske i en viss träffpunkt och även med en viss träffvinkel. För att utvärdera resultaten undersöks och jämförs de två styrlagarnas prestanda och robusthet.

För att kunna utvärdera styrlagarnas egenskaper och jämföra dem implementeras de båda i en befintlig detaljerad simuleringsmiljö, som har utvecklats på Saab Bofors Dynamics i Karlskoga.

De prestanda och robusthetstester som har utförts uppvisar små skillnader på de två styrlagarna och slutsatsen blir därmed att det är möjligt att använda modellbaserad prediktionsreglering vid slutfasstyrning av en viss typ av robot eftersom det sedan tidigare är känt att linjärkvadratisk reglering är en bra styrlag att använda. För att se vilken av de två styrlagarna som är bäst vid slutfasstyrning av en viss typ av robot behöver det göras vissa ändringar och mer detaljerade undersökningar utföras.

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Xu, Shuqi. „Learning Model Predictive Control for Autonomous Racing : Improvements and Model Variation in Model Based Controller“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247881.

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In this work, an improved Learning Model Predictive Control (LMPC)architecture for autonomous racing is presented. The controller is referencefree and is able to improve lap time by learning from history data of previouslaps. A terminal cost and a sampled safe set are learned from history data toguarantee recursive feasibility and non-decreasing performance at each lap.Improvements have been proposed to implement LMPC on autonomousracing in a more efficient and reliable way. Improvements have been doneon three aspects. Firstly, system identification has been improved to be runin a more efficient way by collecting feature data in subspace, so that thesize of feature data set is reduced and time needed to run sorting algorithmcan be reduced. Secondly, different strategies have been proposed toimprove model accuracy, such as least mean square with/without lifting andGaussian process regression. Thirdly, for reducing algorithm complexity,methods combining different model construction strategies were proposed.Also, running controller in a multi-rate way has also been proposed toreduced algorithm complexity when increment of controller frequency isnecessary. Besides, the performance of different system identificationstrategies have been compared, which include strategy from newton’s law,strategy from classical system identification and strategy from machinelearning. Factors that can possibly influence converged result of LMPCwere also investigated, such as prediction horizon, controller frequency.Experiment results on a 1:10 scaled RC car illustrates the effectiveness ofproposed improvements and the difference of different system identificationstrategies.
I detta arbete, presenteras en förbättrad inlärning baserad modell prediktivkontroll (LMPC) för autonom racing, styralgoritm är referens fritt och har visatsig att kunna förbättra varvtid genom att lära sig ifrån historiska data från tidigarevarv. En terminal kostnad och en samplad säker mängd är lärde ifrån historiskdata för att garantera rekursiv genomförbarhet och icke-avtagande prestanda vidvarje varv.förbättringar har presenterats för implementering av LMPC på autonom racingpå ett mer effektivt och pålitligt sätt. Förbättringar har gjorts på tre aspekter.Först, för system identifiering, föreslår vi att samlar feature data i delrummet,så att storlek på samlade datamängd reduceras och tiden som krävs för attköra sorteringsalgoritm minskas. För det andra, föreslår vi olika strategierför förbättrade modellnoggrannheten, såsom LMS med/utan lyft och Gaussianprocess regression. För det tredje, För att reducerar komplexitet för algoritm,metoder som kombinerar olika modellbygg strategier föreslogs. Att körastyrenhet på ett multi-rate sätt har också föreslagits till för att reduceraalgoritmkomplexitet då inkrementet av styrfrekvensen är nödvändigt.Prestanda av olika systemidentifiering har jämförts, bland annat, Newtonslag, klassisk systemidentifierings metoder och strategier från maskininlärning.Faktorer som eventuellt kan påverka konvergens av LMPC resultat har ocksåundersökts. Såsom, prediktions horisont, styrfrekvensen.Experimentresultat på en 1:10 skalad RC-bilen visar effektiviteten hos föreslagnaförbättringarna och skillnaderna i olika systemidentifierings strategier.

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Mann, Gustav, und Jakob Luedtke. „Implementation of a Model Predictive Controller in a Spark-Ignition Engine“. Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176534.

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The propulsion of the spark-ignition engine has been investigated and developed during the past century to improve driveability, minimize fuel consumption and emissions, resulting in highly engineered and computerized powertrains. Well balanced engine maps containing coordinated set-points and model-based information sharing have solved the cross-coupling between different control loops. During transitions between the operating conditions a disadvantageous transient behavior that affects the engine performance may occur. By implementing an MPC as a superior controller a nearly optimal control solution was accomplished. A digital twin of the SI engine was designed through collected measurements and system modeling. The twin made it possible to investigate and elaborate different cost functions in a simulation environment before applying the controller in real-time. By utilizing MPC together with the engine maps a strong relationship between the throttle and iVVT actuator was achieved, which removed the cross-coupling between the actuator control loops and reduced the unfavorable transient behavior.

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Andersson, Amanda, und Elin Näsholm. „Fast Real-Time MPC for Fighter Aircraft“. Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148580.

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The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics. Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program. Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model. The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.

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Osunnuyi, Olufemi Adetunji. „Model predictive control of an exothermic batch reactor using near infrared (NIR) spectroscopic measurements as feedback“. Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/model-predictive-control-of-an-exothermic-batch-reactor-using-near-infrared-nir-spectroscopic-measurements-as-feedback(adf06eb7-438d-4635-a9a4-e2f5f0872693).html.

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Batch and semi-batch processes provide needed flexibility for multi-product plants, especially when products change frequently and production quantities are small. However, challenges occur when trying to implement reliable control systems in batch processes due to some unavoidable inherent characteristics such as the presence of time-varying and nonlinear batch process dynamics and a host of unmeasured disturbances. The most typical control strategy employed in batch process operations does not use utilise online measurements of variables directly related to the product quality and as such is bound to produce off specification products even when the specified control objective has been met. Work done in this thesis is concerned with the design of a supervisory control scheme that takes into consideration the online status of the quality variable of interest from the beginning to the end of the batch process. A novel control methodology is proposed which combines the speed and flexibility of Near-Infrared (NIR) spectroscopic measurements as quality feedback variables within a multiple model predictive control (MPC) framework. In particular the multivariate NIR spectral data is pre-processed for feedback using a statistical model based on Independent Component Analysis (ICA). The proposed controller is tested on a benchmark simulated batch reactor using several case studies and is demonstrated to bring significant improvement in control performance when contrasted with other inferential and direct quality controllers.

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Freiwat, Sami, und Lukas Öhlund. „Fuel-Efficient Platooning Using Road Grade Preview Information“. Thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-270263.

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Platooning is an interesting area which involve the possibility of decreasing the fuel consumption of heavy-duty vehicles. By reducing the inter-vehicle spacing in the platoon we can reduce air drag, which in turn reduces fuel consumption. Two fuel-efficient model predictive controllers for HDVs in a platoon has been formulated in this master thesis, both utilizing road grade preview information. The first controller is based on linear programming (LP) algorithms and the second on quadratic programming (QP). These two platooning controllers are compared with each other and with generic controllers from Scania. The LP controller proved to be more fuel-efficient than the QP controller, the Scania controllers are however more fuel-efficient than the LP controller.

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Rogalsky, Dennis Wayne. „Quantifying plant model parameter effects on controller performance /“. Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9843.

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Evans, Martin A. „Multiplicative robust and stochastic MPC with application to wind turbine control“. Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0ad9b878-00f3-4cfa-a683-148765e3ae39.

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A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.

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Ozbek, Murat Olus. „Inferential Model Predictive Control Of Poly(ethylene Terephthalate) Degradation During Extrusion“. Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/12607497/index.pdf.

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Poly(ethylene terephthalate), PET, which is commonly used as a packaging material, is not degradable in nature. As an issue of sustainable development it must be recycled and converted into other products. During this process, extrusion is an important unit operation. In extrusion process, if the operating conditions are not controlled, PET can go under degradation, which results in the loss of some mechanical properties. In order to overcome the degradation of recycled PET (RPET), this study aims the control of the extrusion process. Dynamic models of the system for control purposes are obtained by experimental studies. In the experimental studies, screw speed, feed rate and barrel temperatures are taken as process variables in the ranges of 50 &ndash
500 rpm, 3.85 &ndash
8.16 g/min and 270 &ndash
310 oC respectively. Singular value decomposition (SVD) technique is used for the best pairing between the manipulated &ndash
controlled variables, where screw speed is taken as the manipulated variable and molecular weight of the product is taken as the controlled variable. PID and model predictive controller (MPC) are designed utilizing the dynamic models in the feedback inferential control algorithm. In the simulation studies, the performance of the designed inferential control system, where molecular weight (Mv) of the product is estimated from the measured intrinsic viscosity ([&
#951
]) of the product, is investigated. The controller utilizing PID and MPC control algorithms are found to be robust and satisfactory in tracking the given set points and eliminating the effects of the disturbances.

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Forel, Alexandre. „Distributed Model Predictive Operation Control of Interconnected Microgrids“. Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206145.

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The upward trends in renewable energy deployment in recent years brings new challengesto the development of electrical networks. Interconnected microgrids appear as a novelbottom-up approach to the production and integration of renewable energy.Using model predictive control (MPC), the energy management of several interconnectedmicrogrids is investigated. An optimisation problem is formulated and distributed ontothe individual units using the alternating direction method of multipliers (ADMM). Themicrogrids cooperate to reach a global optimum using neighbour-to-neighbour communications.The benefits of using distributed operation control for microgrids are analysed and a controlarchitecture is proposed. Two algorithms are implemented to solve the optimisationproblem and their advantages or differences are confronted.
Förnybara energikällor har ökat under senaste åren. Det innebär nya utmaningar förevolutionen av elektriska nät. Microgrids är en bottom-up ansats för produktion ochintegrering av förnybar energi.Energiförsörjning av flera sammankoppladeMicrogrids studeras in detta arbete genommodellbaserad prediktiv kontroll (MPC). Ett optimeringsproblem formuleras på de enskildaenheterna med Alternating DirectionMethod ofMultipliers (ADMM) och parallellberäkningar härledas.Microgrids samarbetar för att nå en global lösning av neighbourto-neighbour kommunikation.Distribuerad energiförsörjning av microgrids analyseras och två kontroll algorithmerutformas.

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49

Hyatt, Phillip Edmond. „Robust Real-Time Model Predictive Control for High Degree of Freedom Soft Robots“. BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8453.

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This dissertation is focused on the modeling and robust model-based control of high degree-of-freedom (DoF) systems. While most of the contributions are applicable to any difficult-to-model system, this dissertation focuses specifically on applications to large-scale soft robots because their many joints and pressures constitute a high-DoF system and their inherit softness makes them difficult to model accurately. First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85\%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks (DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible. We show that this parameterization decreases MPC's sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer's graphics processing unit (GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelized MPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. This adaptive form of MPC is shown to inherit MPC's robustness to completely unmodeled disturbances and adaptive control's ability to decrease trajectory tracking errors over time.

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50

Selvatici, Luca. „Distributed cooperative MPC for aerial robots: a ROS 2 implementation“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The significant decrease in manufacturing costs of hardware components for quadrotors has greatly encouraged research into the design of flight control algorithm for quadrotors, which has seen great growth in recent years. One of the key aspects of the research is the communication between the quadrotors. Nowadays it is considered essential that the quadrotors can communicate with each other. This feature allows numerous advantages: it is possible to generate a network capable of collaborating to solve complex tasks that single quadrotors would not be able to perform, or complete them in a shorter time. The objective of this thesis is the design of a distributed algorithm to control the navigation of a set of quadrotors flying through the same navigation space. A surveillance task has been chosen as a case study, where quadrotors are in charge of arranging themselves in order to protect a target from intruders. Each quadrotor needs to complete both a specific task assigned to it (prevent a certain intruders from reaching the target) and a task in common with the other quadrotors (make sure that the center of the drones coincides with the target and the quadrotors do not collide). With this goal in mind, the project starts with the design of the quadrotor model, controller and trajectories from scratch. Then a Distributed Model Predictive Control algorithm is designed ad hoc to control the navigation of quadrotors. One of the challenges in the creation of this algorithm is the adaptation of the control algorithm to the simultaneous use of Model Predictive Control (MPC) and Online Distributed Gradient Tracking (O-DGT). Indeed, the speed required for the optimization calculations led us to reformulate the MPC in order to make the calculations faster and thus satisfy the limits imposed by the chosen time-step. The proposed model is tested with numerical examples, analyzing a series of cases that allowed us to test different combinations of the developed algorithms.

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