Tesis: "Predictive control ; Process control ; Automatic control"

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Hitzemann, U. "Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter". Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1.

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This Thesis is concerned with model-based control, where models of linear nonminimal state-space (NMSS) and nonlinear state-dependent parameter (SDP) form are considered. In particular, the focus is on model-based predictive control (MPC) in conjunction with the linear NMSS model and on proportional-integralplus (PIP) pole-assignment control in conjunction with the SDP model. The SDP-PIP pole-assignment controller is based on a nonlinear SDP model, however, the approach uses a linear pole-assignment controller design technique. This ‘potential paradox’ is addressed in this Thesis. A conceptual approach to realising the SDP-PIP pole-assignment control is proposed, where an additional conceptual time-shift operator is introduced. This allows the SDPPIP, at each sampling time instance, to be considered as an equivalent linear controller, while operating, in fact, in a nonlinear overall context. Additionally, an attempt to realise SDP-PIP control, where the SDP model exhibits equivalent linear system numerator zeros, is proposed. Regarding the NMSS MPC, emphasis is on square, i.e. equal number of inputs and outputs, multi-input multi-output (MIMO) modelled systems, which exhibit system output cross-coupling effects. Moreover, the NMSS MPC in incremental input form and making use of an integral-of-errors state variable, is considered. A strategy is proposed, that allows decoupling of the system outputs by diagonalising the closed-loop system model via an input transformation. A modification to the NMSS MPC in incremental input form is proposed such that the transformed system input - system output pairs can be considered individually, which allows the control and prediction horizons to be assigned to the individual pairs separately. This modification allows imposed constraints to be accommodated such that the cross-coupling effects do not re-emerge. A practical example is presented, namely, a DC-DC boost converter operating in discontinuous conduction mode (DCM), for which a SDP model is developed. This model is based on measured input-output data rather than on physical relationships. The model incorporates the output current so that the requirements for the load, driven by the converter, is constrained to remain within a predefined output current range. The proposed SDP model is compared to an alternative nonlinear Hammerstein-bilinear structured (HBS) model. The HBS model is, in a similar manner to the SDP model, also based on measured input-output data. Moreover, the differences as well as the similarities of the SDP and HBS model are elaborated. Furthermore, SDP-PIP pole-assignment control, based on the developed SDP model, is applied to the converter and the performance is compared to baseline linear PIP control schemes.

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Henriksson, Erik. "Predictive Control for Wireless Networked Systems in Process Industry". Doctoral thesis, KTH, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141459.

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Wireless networks in industrial process control enable new system architectures and designs. However, wireless control systems can be severely affected by the imperfections of the communication links. This thesis proposes new methods to handle such imperfections by adding additional components in the control loop, or by adapting sampling intervals and control actions. First, the predictive outage compensator is proposed. It is a filter which is implemented at the receiver side of networked control systems. There it generates predicted samples when data are lost, based on past data. The implementation complexity of the predictive outage compensator is analyzed. Simulation and experimental results show that it can considerably improve the closed-loop control performance under communication losses. The thesis continues with presenting an algorithm for controlling multiple processes on a shared communication network, using adaptive sampling intervals. The methodology is based on model predictive control, where the controller jointly decides the optimal control signal to be applied as well as the optimal time to wait before taking the next sample. The approach guarantees conflict-free network transmissions for all controlled processes. Simulation results show that the presented control law reduces the required amount of communication, while maintaining control performance. The third contribution of the thesis is an event-triggered model predictive controller for use over a wireless link. The controller uses open-loop optimal control, re-computed and communicated only when the system behavior deviates enough from a prediction. Simulations underline the methods ability to significantly reduce computation and communication effort, while guaranteeing a desired level of system performance. The thesis is concluded by an experimental validation of wireless control for a physical lab process. A hybrid model predictive controller is used, connected to the physical system through a wireless medium. The results reflect the advantages and challenges in wireless control.

QC 20140217

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3

Bowyer, Robert O. "Multiple order models in predictive control". Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:a5f48d14-0250-4fc6-bb6f-7b43659930e8.

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Predictive control has attracted much attention from both industry and academia alike due to its intuitive time domain formulation and since it easily affords adaption. The time domain formulation enables the user to build in prior knowledge of the operating constraints and thus the process can be controlled more efficiently, and the adaptive mechanism provides tighter control for systems whose behaviour changes with time. This thesis presents a fusion of technologies for dealing with the more practical aspects of obtaining suitable models for predictive control, especially in the adaptive sense. An accurate model of the process to be controlled is vital to the success of a predictive control scheme, and most the of work to date has assumed that this model is of fixed order, a restriction which can lead to poor controller performance associated with under/overparameterisation of the estimated model. To overcome this restriction a strategy which estimates both the parameters and the order of a linear model of the time-varying plant online is suggested. This Multiple Model Least-Squares technique is based on the recent work of Niu and co-workers who have ingeniously extended Bierman's method of UD updating so that, with only a small change to the existing UD update code, a wealth of additional information can be obtained directly from the U and D matrices including estimates of all the lower order models and their loss functions. The algorithm is derived using Clarke's Lagrange multiplier approach leading to a neater derivation and possibly a more direct understanding of Niu's Augmented UD Identification algorithm. An efficient and robust forgetting mechanism is then developed by analysing the properties of the continuous-time differential equations corresponding to existing parameter tracking methods. The resulting Multiple Model Recursive Least-Squares estimator is also ported to the δ-domain in order to obtain models for predictive controllers that employ fast sampling. The MMRLS estimator is then used in an adaptive multiple model based predictive controller for a coupled tanks system to compare performance with the fixed model order case.

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Costa, Thiago Vaz da 1982. "Estudo e implementação de estruturas de controle reconfigurável aplicado a processos químicos". [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266123.

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Orientadores: Flávio Vasconcelos da Silva, Luís Cláudio Oliveira Lopes
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: A consideração sistemática relacionada ao tratamento de falhas na estrutura de controle é essencial para se atingir as metas de um processo ao mesmo tempo seguro e produtivo. Nesse sentido, este trabalho propõe o estudo e a implementação de técnicas baseadas no controle reconfigurável com aplicações em processos químicos. Essa técnica está relacionada à etapa que lida com a falha no sistema em malha fechada, por meio de sua reconfiguração, evitando a evolução dos seus efeitos na estrutura de controle. Nesse estudo, a atenção é voltada ao rastreamento do comportamento nominal do sistema considerando as técnicas baseadas nos atuadores virtuais. O método considera o desvio da planta em relação ao seu comportamento nominal na presença de falhas em seus atuadores. Deste modo, se o distanciamento em relação ao comportamento nominal pode ser estabilizado por meio das redundâncias físicas e analíticas presentes no processo, o comportamento da planta sujeita à falha também pode ser estabilizado. Neste sentido, uma consideração sobre esse desvio no futuro é proposta, permitindo que técnicas baseadas no controle ótimo e preditivo sejam utilizadas. São consideradas especificamente falhas que comprometam o desempenho do processo quando existe a perda total ou parcial de suas variáveis de entrada. Com os componentes comprometidos devidamente localizados, a redistribuição dos sinais de controle da planta é encaminhada para que a estabilidade e desempenho do processo sejam assegurados. A concepção do atuador virtual com horizonte móvel possibilita o uso de técnicas de otimização, permitindo que as informações referentes às características físicas das redundâncias do processo sejam incluídas, evitando a saturação dos componentes utilizados durante o tratamento da falha. A validação da técnica proposta é avaliada experimentalmente em um processo de neutralização, no qual ferramentas de injeção de falhas são capazes de simular os efeitos de falha dos atuadores no processo real. As estratégias estudadas foram implementadas em um software em código aberto e foram testadas experimentalmente a partir do uso de comunicação OPC (Object Linking and Embedding for Process Control). Cenários com diferentes falhas afetando os atuadores do processo foram concebidos e avaliados. Em todos os estudos, observou-se a capacidade da ferramenta em efetuar a reconfiguração do sistema em malha fechada para mitigar os efeitos indesejáveis da falha. Com base nos resultados, conclui-se que a utilização das técnicas em cenários industriais é viável e pode garantir a operação estável do processo mesmo quando surjam falhas em seus componentes, consideradas as condições de controlabilidade e estabilizabilidade do sistema
Abstract: A systematic approach to fault handling in control loops is important to achieve safe and productive process goals. Therefore, the present work proposes the study and implementation of reconfigurable control methods with applications in chemical processes. This technique is related to the fault handling stage in the closed loop system by means of reconfiguration, preventing the progression of fault effects in the control structure. In this study, particular focus is given to virtual actuator based techniques for tracking nominal system behavior. The method considers the deviation of the plant in relation to its nominal behavior when there are faults in its actuators. Therefore, if the nonconformity from the nominal response can be stabilized using the process physical and analytical redundancies, the faulty plant behavior can also be stabilized. Hence, it is proposed the use of the deviation from the nominal system response in the future, enabling the use of optimal and predictive control techniques. Faults affecting process performance due to total or partial loss of input variables are particularly considered. Once the faulty components are located, input signal redistribution is determined in order to ensure process performance and stability. The moving horizon virtual actuator proposal provides the use of optimization techniques allowing information about physical characteristics of the process redundancies to be included, avoiding component saturation during the fault handling approach. The validation of the technique is performed experimentally in a neutralization process, in which fault injection tools are able to simulate the effects of actuators faults in the real process. The studied strategies were implemented using open-source software and tested experimentally by means of an OPC (Object Linking and Embedding for Process Control) communication. Several fault scenarios considering actuator faults were planned and fulfilled for evaluation. The experimental tests showed that the proposed framework is capable of performing closed loop system reconfiguration to mitigate undesirable fault effects. Based on the results, it is concluded that using such a technique in industrial scenarios is feasible and can guarantee stable process operation even when there are faults in its components, considering system controllability and stabilizability conditions
Doutorado
Sistemas de Processos Quimicos e Informatica
Doutor em Engenharia Química

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Costa, Thiago Vaz da 1982. "Controle preditivo baseado em rede de modelos lineares locais aplicado a um reator de neutralização". [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266959.

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Orientadores: Flávio Vasconcelos da Silva, Ana Maria Frattini Fileti
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: Uma malha de controle com baixo desempenho implica em um aumento dos custos de produção, causando descarte de produto fora da especificação e desgaste desnecessário dos elementos finais de controle. A depender do processo controlado, uma malha deficiente pode também acarretar em paradas não previstas na planta e até mesmo em danos ao meio ambiente. Diante do exposto, o controle preditivo baseado em modelos (MPC) é um dos poucos algoritmos comprovadamente capazes de estabilizar processos na presença de não-linearidades e restrições. Para atender aos seus objetivos de controle, o algoritmo clássico MPC utiliza um procedimento de otimização baseado no modelo linear da planta. Contudo, o afastamento da região de projeto do modelo linear resulta na perda de sua efetividade e consequente do controlador que o utiliza. Deste modo, objetivou-se a partir de uma descrição não-linear do sistema a melhoria do desempenho do controlador. Os objetivos específicos dessa dissertação foram o estudo e análise de um controlador GPC (generalized predictive controller) operando em paralelo com uma rede de modelos lineares locais, identificada por meio do algoritmo LOLIMOT (local linear model trees), capaz de adequar o modelo de predição do controlador para a faixa de operação atual do processo. Para a avaliação e análise da qualidade do controlador proposto foi montado um aparato experimental para controle de pH. A estratégia de controle foi implementada em um sistema em código aberto para monitoramento e controle do processo. Portanto, considerando a característica estática não-linear do processo foram realizados estudos comparativos entre o GPC tradicional (baseado em um único modelo linear) e a abordagem proposta. Os resultados mostraram que a rede foi capaz de representar satisfatoriamente a saída do sistema, resultando em uma estrutura simples, com modelos locais na forma de estruturas ARX. Também foi demonstrado que o GPC utilizando a rede de modelos lineares locais desempenhou de forma satisfatória e até mesmo superior ao GPC tradicional.Observou-se que a saída calculada pelo controlador proposto foi consideravelmente menos agressiva que o controlador tradicional, levando a uma considerável diminuição do esforço de controle empregado ao sistema. Os resultados obtidos demonstraram que houve uma economia de até 45% no esforço de controle. Observou-se ainda que o sistema desenvolvido é conveniente para aplicações reais, já que a estratégia de controle preditivo concebida em Scilab obteve sucesso na solução do problema de controle dentro do intervalo de amostragem inclusive quando incorporado o problema QP (programação quadrática) com restrições. Tendo em vista que a estrutura destes sistemas permite que sejam utilizados nas mesmas aplicações destinadas a modelos lineares, comprovou-se também a viabilidade e aplicabilidade do uso das redes de modelos lineares locais diretamente em algoritmos de controle avançado já disponíveis para indústria, como nos controladores GPC
Abstract: Low performance control loops imply in higher production costs, leading to off-specification production loss and unnecessary wear of the final control elements. Depending on the controlled process, the deficient loop can also lead to non-expected plant stops and even on environment damage. In this sense, model predictive control (MPC) is one of the few algorithms proved capable to stabilize processes in the presence of nonlinearities and constraints. To meet its control objectives, the classic MPC algorithm is based on an optimization problem which relies in the system's linear model. Although, the removal of the linear model from its designed condition deteriorates the model's and controller's effectiveness. Hence, the general objective of the presented work relies in the non-linear description of the system for improving the control performance. The main objectives were the study and analysis of a generalized predictive controller (GPC) operating in parallel with a linear local model network, identified by the LOLIMOT algorithm, able to adequate the controller's prediction model for the process operation range. For the quality assessment of the proposed controller, tests were evaluated in an experimental apparatus for pH control. The control strategy was implemented in an open source system for monitoring and control. Therefore, considering the static nonlinear characteristics of the process, comparative studies were applied between the traditional GPC (based on an single global model) and the proposed approach. The results showed that the dynamic network was able to effectively represent the system output, resulting in a simple structure, given the fact that the local models are indeed local ARX models. It was also shown that the GPC using the linear local model network performed satisfactorily and even better than the single model GPC. It was observed that the output calculated by the proposed controller has been considerably less aggressive than the traditional controller, leading to a considerable reduction in the system's control effort. The results showed that there was a saving up to 45% in the control effort. It was also observed that the developed monitoring and control system is suitable for real applications, since the predictive control strategy, implemented in Scilab, succeeded in solving the control problem within the sampling time even with the embedded constrained QP (quadratic programming) problem. Considering that the structure of these systems allows them to be used in similar applications to linear models, it was also proved the viability and applicability of using the linear local models network directly into advanced control algorithms already available to industry as in the GPC controllers
Mestrado
Sistemas de Processos Quimicos e Informatica
Mestre em Engenharia Química

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Clavel, Fanny. "Modélisation et contrôle d'un réfrigérateur cryogénique Application à la station 800W à 4.5K du CEA Grenoble". Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00576608.

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Cette thèse concerne le développement de nouvelles stratégies de contrôle d'unréfrigérateur cryogénique soumis à de fortes variations de charge thermique. De telles perturbationsvont se rencontrer lors du refroidissement des aimants supraconducteurs des futurs réacteurs defusion (tokamak JT-60SA par exemple).La modélisation d'un réfrigérateur de test, offrant une capacité de refroidissement de 800Wà 4.5K, a été effectuée sous le logiciel Matlab/Simulink. Celle-ci est basée sur les équationsthéoriques de la thermodynamique, de la thermique et de l'hydraulique et prend en compte lespropriétés non linéaire de l'hélium à basse température.A partir de ce modèle, une stratégie de contrôle multivariable a été proposée sur les deuxparties du réfrigérateur : la station de compression et la boîte froide. Les résultats expérimentauxmontrent de nettes améliorations et une plus grande stabilité du réfrigérateur en présence decharges pulsées par rapport à la stratégie initiale (PI).Un observateur de la charge thermique du bain d'hélium liquide a également été développé.Le modèle utilisé est construit par identification à partir de mesures internes au réfrigérateur. Ilpourrait servir comme outil de surveillance aux opérateurs.

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Boza, Condorena Edwin Guido 1958. "Identificação de processos e controle preditivo com modelo utilizando técnicas de inteligência artificial aplicadas à produção de bioetanol". [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266648.

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Orientador: Aline Carvalho da Costa
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: Na presente tese foram abordados problemas no contexto da produção de etanol de primeira e segunda geração. No caso de etanol de segunda geração foi abordado o problema de melhorar a eficiência da hidrolise enzimática, com as seguintes contribuições: (1) otimização da carga enzimática utilizando técnicas de modelagem por redes neurais. (2) substituição de um modelo com limitações na representatividade dos pontos experimentais obtido por desenho experimental pela superfície de resposta de um modelo de redes neurais que permite a exploração da região do ótimo no espaço de fatores e respostas. Os resultados obtidos mostram (1) boa precisão para a localização das coordenadas da região ótima de trabalho. (2) um mapeamento da evolução do processo com localização do ótimo global utilizando algoritmos genéticos e técnicas de computação evolutiva. No caso dos processos de produção de etanol de primeira geração foi estudado o problema do controle com modelo de um processo de fermentação continua com extração de etanol utilizando vácuo quando não se tem modelo fenomenológico disponível com as seguintes contribuições e resultados: (1) foi desenvolvida uma abordagem que integrou redes neurais artificiais, com o controle preditivo com modelo (MPC). (2) foram desenvolvidos modelos empíricos do processo com redes neurais artificiais (3) foi desenvolvida uma abordagem que utiliza o "conhecimento aprendido" pelas redes neurais o qual e armazenado em pesos sinápticos e bias. (4) foram desenvolvidos vários modelos empíricos de redes neurais para o monitoramento das concentrações de etanol que podem ser utilizados para desenvolver software sensores. (5) foram implementadas diferentes estruturas de controle preditivo com diferentes modelos internos de redes neurais para os controladores, com otimiza dor linear e não linear para o caso de estudo. Nos diferentes capítulos em que foram implementadas estruturas de controle integrando as redes neurais com a tecnologia MPC, se mostrou que, a abordagem desenvolvida e eficiente para os projetos dos sistemas de controle com modelo empírico
Abstract: In this thesis were studied problems in the context of ethanol production of first and second generation. In the case of second generation ethanol was studied the problem of improving the enzymatic hydrolysis efficiency, with the following contributions: 1) optimization of enzyme loading by using modeling techniques based on neural networks, 2) substitution of a model with limited representation capacity of the experimental points which was obtained by using experimental design by the response surface model of a neural network that allows the exploration of the space of factors and responses. The results show: 1) good accuracy in locating the coordinates of the optimum working region, 2) the mapping of the evolution of the process with the location of the global optimum by using genetic algorithms and evolutionary computing techniques. In case of the production of first generation ethanol was studied the problem of the control with process model of a fermentation process with continuous extraction of ethanol by using a vacuum system when there is no a phenomenological model available with the following contributions and results: 1) It was developed an approach in which were integrated artificial neural networks with model predictive control (MPC). 2) It were developed empirical process models by using artificial neural networks 3) It was developed an approach that uses the "learned knowledge" by neural networks which is stored in synaptic weights and bias. 4) It were developed several empirical models of neural networks for monitoring concentrations of ethanol that can be used to develop software sensors. 5) It were implemented different predictive control structures with different internal models based on neural networks for controllers with linear and non-linear optimizer to be applied on the case of study. In the different chapters in which were implemented control structures by integrating neural networks with MPC technology, it was showed that the developed approach is efficient to be applied in the designs of control systems with empirical model
Doutorado
Desenvolvimento de Processos Químicos
Doutor em Engenharia Química

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Tamagnini, Filippo. "EKF based State Estimation in a CFI Copolymerization Reactor including Polymer Quality Information". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20235/.

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State estimation is an integral part of modern control techniques, as it allows to characterize the state information of complex plants based on a limited number of measurements and the knowledge of the process model. The benefit is twofold: on one hand it has the potential to rationalize the number of measurements required to monitor the plant, thus reducing costs, on the other hand it enables to extract information about variables that have an effect on the system but would otherwise be inaccessible to direct measurement. The scope of this thesis is to design a state estimator for a tubular copolymerization reactor, with the aim to provide the full state information of the plant and to characterize the quality of the product. Due to the fact that, with the existing set of measurements, only a small number of state variables can be observed, a new differential pressure sensor is installed in the plant to provide the missing information, and a model for the pressure measurement is developed. Following, the state estimation problem is approached rigorously and a comprehensive method for analyzing, tuning and implementing the state estimator is assembled from scientific literature, using a variety of tools from graph theory, linear observability theory and matrix algebra. Data reduction and visualization techniques are also employed to make sense of high dimensional information. The proposed method is then tested in simulations to assess the effect of the tuning parameters and measured set on the estimator performance during initialization and in case of estimation with plant-model mismatch. Finally, the state estimator is tested with plant data.

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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation". Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluations required in different parts of the NMPC algorithm. In this work, the problem is tackled using a recently introduced efficient tool, the automatic differentiation (AD). Using the AD tool, a function is evaluated together with all its partial derivative from the code defining the function with machine accuracy. A new NMPC algorithm based on nonlinear least square optimization is proposed. In a first–order method, the sensitivity equations are integrated using a linear formula while the AD tool is applied to get their values accurately. For higher order approximations, more terms of the Taylor expansion are used in the integration for which the AD is effectively used. As a result, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear optimization can be efficiently solved. In many real control cases, the states are not measured and have to be estimated for each instance when a solution of the model equations is needed. A nonlinear extended version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose. The AD tool is used to calculate the required derivatives in the local linearization step of the filter automatically and accurately. Offset is another problem faced in NMPC. A new nonlinear integration is devised for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct the plant/model mismatch. The time response of the controller is also improved as a by–product. The proposed NMPC algorithm has been applied to an evaporation process and a two continuous stirred tank reactor (two–CSTR) process with satisfactory results to cope with large setpoint changes, unmeasured severe disturbances, and process/model mismatches. When the process equations are not known (black–box) or when these are too complicated to be used in the controller, modelling is needed to create an internal model for the controller. In this thesis, a continuous time recurrent neural network (CTRNN) in a state–space form is developed to be used in NMPC context. An efficient training algorithm for the proposed network is developed using AD tool. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve online the optimization problem of the NMPC. The proposed CTRNN and the predictive controller were tested on an evaporator and two–CSTR case studies. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is implemented to control the system. In this work a nonlinear state–space class Wiener model is used to identify the black–box model of the gasifier. A linear model of the plant at zero–load is adopted as a base model for prediction. Then, a feedforward neural network is created as the static gain for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behavior observed in open–loop simulations. By linearizing the neural network at each sampling time, the static nonlinear gain provides certain adaptation to the linear base model. The AD tool is used here to linearize the neural network efficiently. Noticeable performance improvement is observed when compared with pure linear MPC. The controller was able to pass all tests specified in the benchmark problem at all load conditions.

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Rosdal, David. "Missilstyrning med Model Predictive Control". Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2748.

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This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.

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Megías, Jiménez David. "Robustness aspects of Model Predictive Control". Doctoral thesis, Universitat Autònoma de Barcelona, 2000. http://hdl.handle.net/10803/32173.

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Model, Model-based or Receding-horizon Predictive Control (MPC or RHPC) is a successful and mature control strategy which has gained the widespread acceptance of both academia and industry. The basis of these control laws, which have been reported to handle quite complex dynamics, is to perform predictions of the system to be controlled by means of a model. A control profile is then computed to minimise some cost function defined in terms of the predictions and the hypothesised controls. It was soon realised that the first few predictive controllers failed to fulfil essential properties, such as the stability of the nominal closed-loop system. In addition, it was noticed that the discrepancies between the model and the true process, referred to as system uncertainty, can seriously affect the achieved performance. The robustness problem should, thus, be addressed. In this thesis, the problems of nominal stability and robustness are reviewed and investigated. In particular, the accomplishment of constraint specifications in the presence of various sources of uncertainty is a major objective of the methods developed throughout this PhD research. First of all, controllers which guarantee nominal stability, such as the CRHPC and the GPC∞, are highlighted and formulated, and 1-norm counterparts are obtained. The robustness of these strategies in the unconstrained case has been analysed, and it has been concluded that the infinite horizon approach often leads to more convenient performance and robustness results for typical choices of the tuning knobs. Then the constrained case has been undertaken, and min-max controllers based on the global uncertainty approach have been formulated for both 1-norm and 2-norm formulations. For these methods, a band updating algorithm has been suggested to modify the assumed uncertainty bounds on-line. Although both formulations provide similar results, which overcome the classical approach to robustness when constraints are specified, the 1-norm controllers are computationally more efficient, since the optimal control move sequence can be computed with a standard LP problem. Finally, a refinement of the min-max approach which includes the notion that feedback is present in the receding-horizon implementation of predictive controllers, termed as feedback min-max MPC, is shown to overcome some of the drawbacks of the standard min-max approach.
El Control Predictiu Basat en Models (Model, Model-based o Receding-horizon Predictive Control; MPC o RHPC) és una estratègia de control madura i de gran èxit, que ha assolit l'acceptació de les comunitats acadèmica i industrial. La base d'aquest tipus de lleis de control, la capacitat de les quals per treballar amb dinàmiques complexes s'ha documentat en la literatura, és realitzar prediccions del sistema a controlar mitjançant un model. A partir de les prediccions, es calcula un perfil de controls per tal de minimitzar un funció de cost definida en termes de les prediccions i dels controls futurs. Després de les primeres formulacions es van detectar las carències dels controladors predictius per satisfer determinades propietats essencials, com garantir l'estabilitat del sistema nominal en llaç tancat. A més, era ben conegut que les discrepàncies existents entre el model i el procés, denominades incertesa del sistema, podien afectar severament el rendiment. Calia, per tant, abordar el problema de la robustesa. En aquesta tesi es revisa i s'investiguen els problemes de l'estabilitat nominal i la robustesa. En particular, la satisfacció de les especificacions de restriccions en presència de diverses fonts d'incertesa és un objectiu principal dels mètodes desenvolupats al llarg d'aquesta recerca. En primer lloc, s'ha fet una revisió dels controladors que asseguren estabilitat nominal, com el CRHPC i el GPC∞, i s'han suggerit controladors equivalents en norma 1. A continuació, s'ha estudiat la robustesa d'aquestes estratègies en absència de restriccions i s'ha conclòs que l'aproximació d'horitzons infinits condueix, habitualment, a millors resultats pel que fa al rendiment i a la robustesa per a valors típics dels paràmetres de sintonia. Seguidament s'ha tractat el problema de la robustesa en presència de restriccions i s'han formulat controladors min-max, tant en norma 1 com en norma 2, basats en el concepte d'incertesa global. Per a aquests mètodes, s'ha proposat un algorisme d'actualització de les bandes que permet modificar les fites de la incertesa en línia. Tot i que ambdues formulacions proporcionen resultats semblants, que superen els mètodes clàssics de robustesa quan s'especifiquen restriccions, els controladors en norma 1 són més eficients des del punt de vista del temps de còmput, atès que el problema d'optimització es pot resoldre fent servir programació lineal. Finalment, s'han proposat nous controladors basats en un últim avanç de l'aproximació min-max que incorpora la noció que la realimentació és present en la implementació d'horitzó mòbil dels controladors predictius. Aquestes tècniques, anomenades feedback min-max MPC, permeten de superar alguns dels desavantatges de la formulació min-max estàndard.
El Control Predictivo Basado en Modelos (Model, Model-based o Receding-horizon Predictive Control; MPC o RHPC) es una estrategia de control madura y de gran éxito, que ha conseguido la aceptación de las comunidades académica e industrial. La base de este tipo de leyes de control, cuya capacidad para manejar dinámicas complejas se ha documentado en la literatura, es realizar predicciones del sistema a controlar por medio de un modelo. A partir de las predicciones, se calcula un perfil de controles para minimizar una función de coste definida en términos de las predicciones y de los controles futuros. Tras las primeras formulaciones se detectaron las carencias de los controladores predictivos para satisfacer determinadas propiedades esenciales, como garantizar la estabilidad del sistema nominal en lazo cerrado. Además, era bien sabido que las discrepancias existentes entre el modelo y el proceso, denominadas incertidumbre del sistema, podían afectar severamente al rendimiento. El problema de la robustez debía, por tanto, ser abordado. En esta tesis se revisan e investigan los problemas de estabilidad nominal y robustez. En particular, la satisfacción de las especificaciones de restricciones en presencia de varias fuentes de incertidumbre es un objetivo principal de los métodos desarrollados a lo largo de esta investigación. En primer lugar, se han revisado los controladores que aseguran estabilidad nominal, como el CRHPC y el GPC∞ y se han propuesto controladores equivalentes en norma 1. A continuación se ha estudiado la robustez de estas estrategias en ausencia de restricciones y se ha concluido que la aproximación de horizontes infinitos conduce, habitualmente, a mejores resultados en lo referente al rendimiento y a la robustez para valores típicos de los parámetros de sintonía. Seguidamente, se ha tratado el problema de la robustez en presencia de restricciones, y se han formulado controladores min-max, tanto en norma 1como en norma 2, basados en el concepto de incertidumbre global. Para estos métodos, se ha sugerido un algoritmo de actualización de las bandas que permite modificar las cotas de la incertidumbre en línea. Aunque ambas formulaciones proporcionan resultados similares, que superan al enfoque clásico de la robustez cuando se especifican restricciones, los controladores en norma 1 son más eficientes desde el punto de vista de tiempo de cómputo, puesto que el problema de optimización se puede resolver usando programación lineal. Finalmente, se han propuesto otros controladores basados en un último avance de la aproximación min-max que incorpora la noción de que la realimentación está presente en la implementación de horizonte móvil de los controladores predictivos. Estas técnicas, denominadas feedback min-max MPC, permiten superar algunas de las desventajas de la formulación min-max estándar.

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12

Ibrahim, Kamarul Asri. "Active statistical process control". Thesis, University of Newcastle Upon Tyne, 1989. http://hdl.handle.net/10443/407.

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Most Statistical Process Control (SPC) research has focused on the development of charting techniques for process monitoring. Unfortunately, little attention has been paid to the importance of bringing the process in control automatically via these charting techniques. This thesis shows that by drawing upon concepts from Automatic Process Control (APC), it is possible to devise schemes whereby the process is monitored and automatically controlled via SPC procedures. It is shown that Partial Correlation Analysis (PCorrA) or Principal Component Analysis (PCA) can be used to determine the variables that have to be monitored and manipulated as well as the corresponding control laws. We call this proposed procedure Active SPC and the capabilities of various strategies that arise are demonstrated by application to a simulated reaction process. Reactor product concentration was controlled using different manipulated input configurations e.g. manipulating all input variables, manipulating only two input variables, and manipulating only a single input variable. The last two manipulating schemes consider the cases when all input variables can be measured on-line but not all can be manipulated on-line. Different types of control charts are also tested with the new Active SPC method e.g. Shewhart chart with action limits; Shewhart chart with action and warning limits for individual observations, and lastly the Exponentially Weighted Moving Average control chart. The effects of calculating control limits on-line to accommodate possible changes in process characteristics were also studied. The results indicate that the use of the Exponentially Weighted Moving Average control chart, with limits calculated using Partial Correlations, showed the best promise for further development. It is also shown that this particular combination could provide better performance than the common Proportional Integral (PI) controller when manipulations incur costs.

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13

Hartley, Edward Nicholas. "Model predictive control for spacecraft rendezvous". Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609090.

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14

Ainscough, D. M. "Automatic control of weld penetration". Thesis, University of Liverpool, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234822.

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15

Wang, Shensheng. "Weighting normalization in optimal predictive control /". free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025659.

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16

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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17

Taysom, Brandon Scott. "Temperature Control in Friction Stir Welding Using Model Predictive Control". BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/6027.

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Temperature is a very important process parameter in Friction Stir Welding (FSW), but until lately active control of temperature has not been practiced. Recently, temperature control via a PID controller has proven to be effective. Model Predictive Control (MPC) is a control method that holds promise, but has not been attempted in FSW before. Two different model forms are developed for MPC and are evaluated. The first is a simple first-order plus dead time (FOPDT) model. The second is the Hybrid Heat Source model and is more complex; it combines the heat source method and a 1D discretized thermal model of the FSW tool. Model parameters were determined by fitting model predictions to actual weld data. The models were evaluated for their performance in modeled and unmodeled disturbances once the process was already at a quasi steady state condition and also were evaluated for control immediately after plunge. The FOPDT based MPC controller has very good performance and was comparable in performance to previously proven and well-tuned PID controllers. For small modeled disturbances the FOPDT controller settled within 1°C of the setpoint in 10s with almost no oscillations and only 2°C of overshoot. For large unmodeled disturbances, the FOPDT controller settled within 1°C of the setpoint in 30s with no oscillations and 16°C of overshoot. For the same disturbances, the PID servo controller settled in 30s with no oscillations and 9°C of overshoot, and the PID regulator controller settled in 15s but had almost a full oscillation and 13°C of overshoot.The Hybrid Heat Source MPC controller and the PID regulator controller were also able to control temperature within 5°C of the setpoint immediately after the plunge during the highly transient portion of the weld, which previously had been assumed to be too difficult to control. The PID regulator controller had a high degree of variability between the two runs (a settling time of 10s and 30s, and .5 and 4.5 oscillations before settling), but settled quickly and once settled was able to hold the temperature within 2°C of the setpoint. The HHS MPC controller on the other hand had far fewer oscillations (0 and 1 oscillation) before settling, but could only hold the temperature within 5°C of the setpoint. Both of these controllers performed far better than the FOPDT MPC and PID servo controllers.

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18

Lambert, E. P. "Process control applications of long-range prediction". Thesis, University of Oxford, 1987. http://ora.ox.ac.uk/objects/uuid:de56df0b-466c-42ce-a03b-72228ad6af2a.

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The recent Generalised Predictive Control algorithm (Clarke et al, 1984,87) is a self-tuning/ adaptive control algorithm that is based upon long-range prediction, and is thus claimed to be particularly suitable for process control application. The complicated nature of GPC prevents the application of standard analytical techniques. Therefore an alternative technique is developed where an equivalent closed loop expression is repeatedly calculated for various control scenarios. The properties of GPC are investigated and, in particular, it is shown that 'default' values for GPC's design parameters give a mean-level type of control law that can reasonably be expected to provide robust control for a wide variety of processes. Two successful industrial applications of GPC are then reported. The first series of trials involve the SISO control of soap moisture for a full-scale drying process. After a brief period of PRBS assisted self-tuning default GPC control performance is shown to be significantly better than the existing manual control, despite the presence of a large time-delay, poor measurements and severe production restrictions. The second application concerns the MIMO inner loop control of a spray drying tower using two types of GPC controller: full multivariable MGPC, and multi-loop DGPC. Again after only a brief period of PRBS assisted self-tuning both provide dramatically superior control compared to the existing multi-loop gain-scheduled PID control scheme. In particular the use of MGPC successfully avoids any requirement for a priori knowledge of the process time-delay structure or input-output pairing. The decoupling performance of MGPC is improved by scaling and that of DGPC by the use of feed-forward. The practical effectiveness of GPC's design parameters (e.g. P, T and λ) is also demonstrated. On the estimation side of adaptive control the current state-of-the-art algorithms are reviewed and shown to suffer from problems such as 'blowup', parameter drift and sensitivity to unmeasurable load disturbances. To overcome these problems two novel estimation algorithms (CLS and DLS) are developed that extend the RLS cost-function to include weighting of estimated parameters. The exploitation of the 'fault detection' properties of CLS is proposed as a more realistic estimation philosophy for adaptive control than the 'continuous retention of adaptivity'.

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19

Drca, Ivana. "Nonlinear Model Predictive Control of the Four Tank Process". Thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-106237.

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Model predictive control techniques are widely used in the process industry. They are considered methods that give good performance and are able to operate during long periods without almost any intervention. Model predictive control is also the only technique that is able to consider model restrictions. Almost all industrial processes have nonlinear dynamics, however most MPC applications are based on linear models. Linear models do not always give a sufficiently adequate representation of the system and therefore nonlinear model predictive control techniques have to be considered. Working with nonlinear models give rise to a wide range of difficulties such as, non convex optimization problems, slow processes and a different approach to guarantee stability . This project deals with nonlinear model predictive control and is written at the University of Seville at the department of Systems and Automatic control and at the department of Automatic Control at KTH. The first objective is to control the nonlinear Four Tank Process using nonlinear model predictive control. Objective number two is to investigate if and how the computational time and complexity can be reduced. Simulations show that a nonlinear model predictive control algorithm is developed with satisfactory results. The algorithm is fast enough and all restrictions are respected for initial state values inside of the terminal set as well as for initial state values outside of the terminal set. Feasibility and stability is ensured for both short as well as for longer prediction horizon, guaranteeing that the output reaches the reference. Hence the choice of a short respectively long prediction horizon is a trade off between shorter computational time versus better precision. Regarding the reduction of the computational time, penalty functions have been implemented in the optimization problem converting it to an unconstrained optimization problem including a PHASE-I problem. Results show that this implementation give approximately the same computational time as for the constrained optimization problem. Precision is good for implementations with penalty functions both for long and short prediction horizons and initial state values inside and outside of the terminal set.

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20

Ashobi, Mohammad. "Modeling and control of a continuous crystallization process using neural networks and model predictive control". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24049.pdf.

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21

Charos, Georgios Nikolaou. "Model predictive constrained control : development, implementation, and decentralization". Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/11311.

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22

Imsland, Lars. "Topics in nonlinear control. : Output Feedback Stabilization and Control of Positive Systems". Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-355.

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The contributions of this thesis are in the area of control of systems with nonlinear dynamics. The thesis is divided into three parts. The two first parts are similar in the sense that they both consider output feedback of rather general classes of nonlinear systems, and both approaches are based on mathematical programming (although in quite different ways). The third part contains a state feedback approach for a specific system class, and is more application oriented.

The first part treats control of systems described by nonlinear difference equations, possibly with uncertain terms. The system dynamics are represented by piecewise affine difference inclusions, and for this system class, piecewise affine controller structures are suggested. Controller synthesis inequalities for such controller structures are given in the form of Bilinear Matrix Inequalities (BMIs). A solver for the BMIs is developed. The main contribution is to the output feedback case, where an observer-based controller structure is proposed. The theory is exemplified through two examples.

In the second part the output feedback problem is examined in the setting of Nonlinear Model Predictive Control (NMPC). The state space formulation of NMPC is inherently a state feedback approach, since the state is needed as initial condition for the prediction in the controller. Consequently, for output feedback it is natural to use observers to obtain estimates of the state. A high gain observer is applied for this purpose. It is shown that for several existing NMPC schemes, the state feedback stability properties ``semiglobally'' hold in the output feedback case. The theory is illuminated with a simple example.

Finally, a state feedback controller for a class of positive systems is proposed. Convergence of the state to a certain subset of the first orthant, corresponding to a constant ``total mass'' (interpreting states as masses) is obtained. Conditions are given under which convergence to this set implies asymptotic stability of an equilibrium. Simple examples illustrate some properties of the controller. Furthermore, the control strategy is applied to the stabilization of a gas-lifted oil well, and simulations on a rigorous multi-phase dynamic simulator of such a well demonstrate the controller performance.

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23

MacKay, Maria Ellen. "Model based predictive control of nonlinear and multivariable systems". Thesis, Manchester Metropolitan University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337269.

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24

Schön, Tomas. "Identification for Predictive Control : A Multiple Model Approach". Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1050.

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Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry.

This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon.

The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions.

Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.

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25

Droge, Greg Nathanael. "Behavior-based model predictive control for networked multi-agent systems". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51864.

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We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

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26

Riviello, Luca. "Rotorcraft trim by a neural model-predictive auto-pilot". Thesis, Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04142005-203616/unrestricted/riviello%5Fluca%5F200505%5Fmast.pdf.

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Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2005.
Bottasso, Carlo, Committee Chair ; Hodges, Dewey, Committee Member ; Bauchau, Olivier, Committee Member. Includes bibliographical references.

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27

Byseke, David y Alexander Thunell. "Automatic monitoring and control of Laser Metal Deposition Process". Thesis, Högskolan Väst, Avdelningen för Industriell ekonomi, Elektro- och Maskinteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-16745.

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Laser metal deposition is an additive manufacturing technique that enables the manufacturing or repair of high-quality metal parts by building fine layers one at a time. To get a stable process with a low number of flaws and irregularities the process needs a fully operational and functioning control system. At PTC in Trollhättan, a production research facility that is a department of University West, several experiments have previously been conducted with an LMD machine. The main objective of this thesis is to deliver input from available methods for automatic control and monitoring of the LMD process. The available methods are explained in the report and previous experiments that have been conducted have been documented in this thesis. Another objective of the thesis is to develop a prototype for monitoring and control of the process. Previous work has mainly used a visual-based control system that has used CMOS-, CCD-, or an infrared camera. Pyrometers and structured light scanning have also been used. Non-optical methods such as acoustical sensors and thermocouples have also been used for monitoring and control. With the gathered information about the available control methods, a prototype has been developed to automatically control the LMD machine located at PTC. The control uses a CMOS camera to gather live imaging from the machine in order to adjust machine parameters, in real-time, to automatically control the process. The different parameters have a strong correlation to the final machine output and are also explained in the thesis. The prototype and the gathering of data from the process have been made using Labview as an image-processing software. An evaluation of the developed prototype has been made and the different control methods have been discussed. The developed prototype measures the melt pool by using an algorithm that counts the number of pixels in the melt pool. However, further research needs to be made to determine if the measured width correlates with the actual width of the cladded string.

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28

Simminger, Jerome C. "A constrained multivariable nonlinear predictive controller". Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/10152.

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29

Yang, Xiaoke. "Fault-tolerant predictive control : a Gaussian process model based approach". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708784.

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30

Andersson, Emma. "Intuitive Mission Handling with Automatic Route Re-planning using Model Predictive Control". Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80638.

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The system for mission handling in the Gripen fighter aircraft, and in its ground supporting system, consists for example of ways to plan mission routes, create mission points and validate performed missions. The system is complex and for example, the number of different mission points used increases due to changing demands and needs. This master thesis presents suggestions for improvements and simplifications for the mission handling system, to make it more intuitive and more friendly to use. As a base for the suggestions, interviews with pilots from Saab, TUJAS and FMV have been conducted, this is to obtain opinions and ideas from those using the system and have deep knowledge about it. Another possible assistance and improvement is to provide the possibility of on-line automatic re-planning of the mission route in case of obstacles. MPC (Model Predictive Control) has been used to estimate the obstacle’s flight path,and calculate a new route to the next mission point which does not conflict with the estimated enemy’s path. This system has been implemented in Matlab and the concept is demonstrated with different test scenarios where the design parameters (prediction horizon and penalty in the cost function) for the controller are varied, and stationary and moving obstacles are induced.
Systemet för uppdragshantering i stridsflygplanet Gripen, och i dess markstödsystem, består bland annat av uppdragsplanering, skapande av uppdragspunkter och möjligheter att validera utförda uppdrag. Systemet är komplext och exempelvis växer antalet uppdragspunkter med omvärldens ökande krav och behov. Detta examensarbete presenterar förslag till förenklingar och förbättringar i uppdragshanteringssystemet, för att göra det mer intuitivt och användarvänligt. Som grund för förslagen har intervjuer med piloter från Saab, TUJAS och FMV gjorts, för att samla in åsikter och idéer från de som använder systemet och har bred kunskap om det. En förbättring är en möjlighet till online automatisk omplanering av uppdragsrutten vid hinder. MPC (modellbaserad prediktionsreglering) har använts för att estimera den dynamiska fiendens flygväg, och beräkna en ny rutt till nästa uppdragspunkt som inte ligger i konflikt med den estimerade vägen för hindret. Detta system har implementerats i Matlab och konceptet demonstreras med olika testscenarion där prestandaparametrar (prediktionshorisont och straff i kostnadsfunktionen) för regulatorn varieras, och stationära och rörliga hinder induceras.

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31

Haznedar, Baris. "Reduced order infinite horizon Model Predictive Control of sheet forming processes". Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/11222.

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32

Hellström, Erik. "Look-ahead Control of Heavy Trucks utilizing Road Topography". Licentiate thesis, Linköping University, Linköping University, Vehicular Systems, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9262.

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The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined by the ratio and the road topography. If further variations are actuated by a controller, there is a potential to lower the fuel consumption by taking the upcoming topography into account. This possibility is explored through theoretical and simulation studies as well as experiments in this work.

Look-ahead control is a predictive strategy that repeatedly solves an optimization problem online by means of a tailored dynamic programming algorithm. The scenario in this work is a drive mission for a heavy diesel truck where the route is known. It is assumed that there is road data on-board and that the current heading is known. A look-ahead controller is then developed to minimize fuel consumption and trip time.

The look-ahead control is realized and evaluated in a demonstrator vehicle and further studied in simulations. In the prototype demonstration, information about the road slope ahead is extracted from an on-board database in combination with a GPS unit. The algorithm calculates the optimal velocity trajectory online and feeds the conventional cruise controller with new set points. The results from the experiments and simulations confirm that look-ahead control reduces the fuel consumption without increasing the travel time. Also, the number of gear shifts is reduced. Drivers and passengers that have participated in tests and demonstrations have perceived the vehicle behavior as comfortable and natural.

Report code: LIU-TEK-LIC-2007:28.

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33

Blasco, Ferragud Francesc Xavier. "Control predictivo basado en modelos mediante técnicas de optimización heurística. Aplicación a procesos no lineales y multivariables". Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/15995.

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La Tesis Doctoral se fundamenta, principalmente, en la exploración de nuevos métodos de Control Predictivo Basado en Modelos (MBPC) mediante la incorporación de herramientas de optimización heurística y las mejoras en las prestaciones que se pueden conseguir con ello. La metodología de MBPC constituye un campo cada vez más importante en el control de procesos debido a que se trata de una formulación muy intuitiva, y a la vez muy potente, de un problema de control (por tanto es más fácilmente aceptable en el ámbito industrial). A pesar de ello, presenta limitaciones cuando se quiere aplicar a ciertos procesos complejos. Un elemento fundamental y al mismo tiempo limitante de ésta metodología lo constituye la técnica de optimización que se utilice. Simplificando mucho, el MBPC se convierte en un problema de minimización en cada periodo de muestreo, y la complejidad del problema de control se refleja directamente en la función a minimizar en cada instante. Si se incorporan modelos no lineales, restricciones en las variables, e índices de funcionamiento sofisticados, todo ello asociado a los problemas de tiempo real, se va a requerir algoritmos de optimización adecuados que garanticen el mínimo global en un tiempo acotado. En este sentido, la tesis incluye un análisis de las metodologías de Optimización Heurísticas, Simulated Annealing y Algoritmos Genéticos, como candidatas a la resolución de ese tipo de problemas y apartir de ellas realiza una implementación novedosa (denominada ASA) dentro del grupo de los algoritmos de Simulated Annealing que reduce el coste computacional. En los Algoritmos Genéticos, se obtienen las combinaciones de codificación y operadores genéticos más adecuadas para conseguir buenas relaciones de 'calidad de la solución/coste computacional' en la resolución de problemas de minimización complejos (no convexos, con discontinuidades, restricciones, etc.). Todo este análisis previo, permite la adaptación adecuada de estas técnicas heurísticas......
Blasco Ferragud, FX. (1999). Control predictivo basado en modelos mediante técnicas de optimización heurística. Aplicación a procesos no lineales y multivariables [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15995
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34

Stewart, Brandon D. "Bringing automatic stereotyping under control implementation intentions as efficient means of thought control /". Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1189544075.

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Shah, Dhaval. "DELAY MODELING AND LONG-RANGE PREDICTIVE CONTROL OF CZOCHRALSKI GROWTH PROCESS". Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2118.

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This work presents the Czochralski growth dynamics as time-varying delay based model, applied to the growth of La3Ga5.5Ta0.5O14 (LGT) piezoelectric crystals. The growth of high-quality large-diameter oxides by Czochralski technique requires the theoretical understanding and optimization of all relevant process parameters, growth conditions, and melts chemistry. Presently, proportional-integral- derivative (PID) type controllers are widely accepted for constant-diameter crystal growth by Czochralski. Such control systems, however, do not account for aspects such as the transportation delay of the heat from crucible wall to the crystal solidification front, heat radiated from the crucible wall above the melt surface, and varying melt level. During crystal growth, these time delays play a dominant role, and pose a significant challenge to the control design. In this study, a time varying linear delay model was applied to the identification of nonlinearities of the growth dynamics. Initial results reveled the benefits of this model with actual growth results. These results were used to develop a long-range model predictive control system design. Two different control techniques using long range prediction are studied for the comparative study. Development and testing of the new control system on real time growth system are discussed in detail. The results are promising and suggest future work in this direction. Other discussion about the problems during the crystal growth, optimization of crystal growth parameters are also studied along with the control system design.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD

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36

Coetzee, Lodewicus Charl. "Robust model predictive control of an electric arc furnace refining process". Diss., Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-08212007-145804.

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37

Agarwal, Naveen. "Nonlinear model predictive control of a semi-batch emulsion polymerization reactor". Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/8456.

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38

Zheng, Qing. "On Active Disturbance Rejection Control: Stability Analysis and Applications in Disturbance Decoupling Control". Cleveland, Ohio : Cleveland State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=csu1250274093.

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Thesis (D.Eng.)--Cleveland State University, 2009.
Abstract. Title from PDF t.p. (viewed on Oct. 26, 2009). Includes bibliographical references (p. 78-89). Available online via the OhioLINK ETD Center and also available in print.

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39

Bell, Michael Ray. "Fuzzy logic control of uncertain industrial processes". Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.

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40

李泉志 y Chuen-chi Lee. "The control of a varying gain process using a varying sampling-rate PID controller with application to pH control". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31211598.

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41

Lee, Chuen-chi. "The control of a varying gain process using a varying sampling-rate PID controller with application to pH control /". [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1366573X.

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42

Nunes, Giovani Cavalcanti. "Design and analysis of multivariable predictive control applied to an oil-water-gas separator a polynomial approach /". [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1585/Phd.pdf.

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Thesis (Ph. D.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains viii, 118 p.; also contains graphics. Vita. Includes bibliographical references (p. 115-117).

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43

Ozyurt, Gokhan. "3-d Humanoid Gait Simulation Using An Optimal Predictive Control". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606534/index.pdf.

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In this thesis, the walking of a humanoid system is simulated applying an optimal predictive control algorithm. The simulation is built using Matlab and Simulink softwares. Four separate physical models are developed to represent the single support and the double support phases of a full gait cycle. The models are three dimensional and their properties are analogous to the human&rsquo
s. In this connection, the foot models in the double support phases include an additional joint which connects the toe to the foot. The kinematic relationships concerning the physical models are formulated recursively and the dynamic models are obtained using the Newton &ndash
Euler formulation. The computed torque method is utilized at the level of joints. In the double support phase, the redundancy problem is solved by the optimization of the actuating torques. The command accelerations required to control the gait are obtained by applying an optimal predictive control law. The introduced humanoid walker achieves a sustainable gait by tuning the optimization and prediction parameters. The control algorithm manages the tracking of the predefined walking pattern with easily realizable joint accelerations. The simulation is capable of producing all the reaction forces, reaction moments and the values of the other variables. During these computations, a three dimensional view of the humanoid walker is animated simultaneously. As a result of this study, a suitable simulation structure is obtained to test and improve the mechanical systems which perform bipedal locomotion. The modular nature of the simulation structure developed in this study allows testing the performance of alternative control laws as well.

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44

Hoffman, David William. "Bursting strength control on a linerboard machine". Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63326.

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45

Botha, Paul Jacobus. "Detecting change in complex process systems with phase space methods". Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019/508.

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46

Lu, Yaohui. "Scheduling quasi-min-max model predictve control". Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/11692.

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47

Wang, Hui. "Error equivalence theory for manufacturing process control". [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002252.

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48

Hong, Feng. "Multivariable predictive control development and application in food extrusion processes /". free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9946262.

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49

Rezvani, Arany Roushan. "Gaussian Process Model Predictive Control for Autonomous Driving in Safety-Critical Scenarios". Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161430.

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This thesis is concerned with model predictive control (MPC) within the field of autonomous driving. MPC requires a model of the system to be controlled. Since a vehicle is expected to handle a wide range of driving conditions, it is crucial that the model of the vehicle dynamics is able to account for this. Differences in road grip caused by snowy, icy or muddy roads change the driving dynamics and relying on a single model, based on ideal conditions, could possibly lead to dangerous behaviour. This work investigates the use of Gaussian processes for learning a model that can account for varying road friction coefficients. This model is incorporated as an extension to a nominal vehicle model. A double lane change scenario is considered and the aim is to learn a GP model of the disturbance based on previous driving experiences with a road friction coefficient of 0.4 and 0.6 performed with a regular MPC controller. The data is then used to train a GP model. The GPMPC controller is then compared with the regular MPC controller in the case of trajectory tracking. The results show that the obtained GP models in most cases correctly predict the model error in one prediction step. For multi-step predictions, the results vary more with some cases showing an improved prediction with a GP model compared to the nominal model. In all cases, the GPMPC controller gives a better trajectory tracking than the MPC controller while using less control input.

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50

Carlsson, Oscar. "Utveckling av modellbaserad reglering i kommersiella styrsystem". Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17943.

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In industrial control systems PID-control remains the prevalent strategy, also for processes that would benefit from model based control. The purpose of this thesis is to evaluate whether model based control can be readily implemented in an industrial control system. To this end a simulated surge tank with a simulated industrial control system is studied. For evaluation two scenarios with specified objectives are selected.

Following a review of LQR and versions of MPC, Predictive Functional Control (PFC) is considered the most suitable for implementation. PFC is a form of MPC developed with industrial applications in mind and therefore has several advantages for implementation in an industrial control system. Controllers for the surge tank-system are developed and implemented in the control system.

Basic analysis of stability, sensitivity and robustness suggests that PFC has some advantages that might be important in a non-simulated implementation. Compared with PID-controllers adjusted for control performance, PFC does not show any notable improvements in performance.

In conclusion, it is possible to implement model based control in an industrial control system and with PFC the implementation is considered easy.

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Tesis sobre el tema "Predictive control ; Process control ; Automatic control"

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