Academic literature on the topic 'Model predictive controller (MPC)'
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Journal articles on the topic "Model predictive controller (MPC)"
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Rezaee, Alireza. "Model predictive Controller for Mobile Robot." Transactions on Environment and Electrical Engineering 2, no. 2 (2017): 18. http://dx.doi.org/10.22149/teee.v2i2.96.
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This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC illustrates advantage of the designed controller and its ability for exact control of the robot on a specified guide path.
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Alasali, Feras, Stephen Haben, Husam Foudeh, and William Holderbaum. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads." Energies 13, no. 10 (2020): 2596. http://dx.doi.org/10.3390/en13102596.
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This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems.
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Wahid, Abdul, and Richi Adi. "MODELING AND CONTROL OF MULTIVARIABLE DISTILLATION COLUMN USING MODEL PREDICTIVE CONTROL USING UNISIM." SINERGI 20, no. 1 (2016): 14. http://dx.doi.org/10.22441/sinergi.2016.1.003.
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Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation columns by UNISIM R390.1 software. The optimization process was carried out by tuning the MPC controller parameters such as sampling time (Ts = 1 – 240 s), prediction horizon (P = 1-400), and the control horizon (M=1-400). The comparison between the performance of MPC and PI controller is presented and Integral Absolut Error (IAE) was used as comparison parameter. The results indicate that the performance of MPC was better than PI controller for set point change 0.95 to 0.94 on distillate product composition using a modified model 1 with IAE 0.0584 for MPC controller and 0.0782 for PI controller.
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Xu, Ying, Wentao Tang, Biyun Chen, Li Qiu, and Rong Yang. "A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles." Symmetry 13, no. 3 (2021): 381. http://dx.doi.org/10.3390/sym13030381.
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Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.
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Kümpel, Alexander, Phillip Stoffel, and Dirk Müller. "Self-adjusting model predictive control for modular subsystems in HVAC systems." Journal of Physics: Conference Series 2042, no. 1 (2021): 012037. http://dx.doi.org/10.1088/1742-6596/2042/1/012037.
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Abstract In order to reduce the energy consumption and CO2 emissions in the building sector, an efficient control strategy, such as model predictive control (MPC) is required. However, MPC is rarely applied in buildings since the implementation and modeling is complex, time consuming and costly. To bring MPC into practice, controllers and models are needed, that automatically adapt their behavior to the controlled system. In this work, such a self-adjusting MPC applicable to heating, ventilation and air-conditioning (HVAC) systems is developed. The MPC is based on a simple grey-box model that is able to cover the general dynamics of the considered subsystem. The controller adapts the model parameters online according to the past measurements of the controlled system using a moving horizon estimation. The developed self-adjusting MPC is applied to three heating coils in a simulation. Compared with a PID controller, the self-adjusting MPC is able to increase the control quality up to 10%, while no manual tuning is needed. Additionally, the model predictive approach is able to reduce the power consumption of the pump by 80%.
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Chrif, Labane, and Zemalache Meguenni Kadda. "Aircraft Control System Using Model Predictive Controller." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 259. http://dx.doi.org/10.11591/tijee.v15i2.1538.
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This paper concerns the application of model-based predictive control to the longitudinal and lateral mode of an aircraft in a terrain following task. The predictive control approach was based on a quadratic cost function and a linear state space prediction model with input and state constraints. The optimal control was obtained as the solution of a quadratic programming problem defined over a receding horizon. Closed-loop simulations were carried out by using the linear aircraft model. This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industrial model predictive control technology has been presented first followed by a some concepts like the receding horizon, moves etc. which form the basis of the MPC. It follows the Optimization problem which ultimately leads to the description of the Dynamic Matrix Control (DMC).The MPC presented in this report is based on DMC. After this the application summary and the limitations of the existing technology has been discussed and the next generation MPC, with an emphasis on potential business and research opportunities has been reviewed. Finally in the last part we generate Matlab code to implement basic model predictive controller and introduce noise into the model.
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Lio, Wai Hou, John Anthony Rossiter, and Bryn Llywelyn Jones. "Modular Model Predictive Control upon an Existing Controller." Processes 8, no. 7 (2020): 855. http://dx.doi.org/10.3390/pr8070855.
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The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.
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Kumavat, Mayur, and Sushil Thale. "Analysis of CSTR Temperature Control with PID, MPC & Hybrid MPC-PID Controller." ITM Web of Conferences 44 (2022): 01001. http://dx.doi.org/10.1051/itmconf/20224401001.
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This paper presents an analysis of the continuous stirred tank reactor (CSTR) temperature control with the Proportional-Integral-Derivative (PID) Controller, Model Predictive Controller (MPC) and Hybrid-Model Predictive Controller-Proportional Integral Derivative Controller (MPC-PID). It is the main goal of this project to find a suitable improvement strategy for the system’s stability and accuracy to be more stable. By creating a model, the control system is implemented for all the above mentioned control methods and so comparative analysis is carried out to find the best control method for CSTR. Simulation data inspector is used to compare the performance of different types of control systems: PID, MPC and MPC-PID. It has been observed that the hybrid MPC-PID has a more effective control action than a PID controller; with some tuning, the MPC controller can maintain the temperature within a reference or set point range. The control and simulation toolbox is used to construct the model predictive control method in LabVIEW platform. The performance of controllers is measured in terms of settling time, rise time and percentage of overshoot. Finally, a comparative overview of PID, MPC and Hybrid MPC-PID controllers on system performance is presented and discussed.
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Munoz, Samuel Arce, Junho Park, Cristina M. Stewart, Adam M. Martin, and John D. Hedengren. "Deep Transfer Learning for Approximate Model Predictive Control." Processes 11, no. 1 (2023): 197. http://dx.doi.org/10.3390/pr11010197.
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Transfer learning is a machine learning technique that takes a pre-trained model that has already been trained on a related task, and adapts it for use on a new, related task. This is particularly useful in the context of model predictive control (MPC), where deep transfer learning is used to improve the training of the MPC by leveraging the knowledge gained from related controllers. One way in which transfer learning is applied in the context of MPC is by using a pre-trained deep learning model of the MPC, and then fine-tuning the controller training for a new process automation task. This is similar to how an equipment operator quickly learns to manually control a new processing unit because of related skills learned from controlling the prior unit. This reduces the amount of data required to train the approximate MPC controller, and also improves the performance on the target system. Additionally, learning the MPC actions alleviates the computational burden of online optimization calculations, although this approach is limited to learning from systems where an MPC has already been developed. The paper reviews approximate MPC formulations with a case study that illustrates the use of neural networks and transfer learning to create a multiple-input multiple-output (MIMO) approximate MPC. The performance of the resulting controller is similar to that of a controller trained on an existing MPC, but it requires less than a quarter of the target system data for training. The main contributions of this paper are a summary survey of approximate MPC formulations and a motivating case study that includes a discussion of future development work in this area. The case study presents an example of using neural networks and transfer learning to create a MIMO approximate MPC and discusses the potential for further research and development in this area. Overall, the goal of this paper is to provide an overview of the current state of research in approximate MPC, as well as to inspire and guide future work in transfer learning.
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Vrečko, D., N. Hvala, and M. Stražar. "The application of model predictive control of ammonia nitrogen in an activated sludge process." Water Science and Technology 64, no. 5 (2011): 1115–21. http://dx.doi.org/10.2166/wst.2011.477.
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In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control.
Dissertations / Theses on the topic "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.<br>Master of Science
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Mattsson, Mathias, and 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.<br>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.<br>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.<br>Dissertation (MEng)--University of Pretoria, 2020.<br>Electrical, Electronic and Computer Engineering<br>MEng<br>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.<br>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, and 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.<br>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<br>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.<br>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.
Books on the topic "Model predictive controller (MPC)"
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Takács, Gergely. Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. Springer London, 2012.
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Khaled, Nassim, and Bibin Pattel. Practical Design and Application of Model Predictive Control: MPC for MATLAB and Simulink Users. Butterworth-Heinemann Limited, 2018.
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Takács, Gergely, and Boris Rohaľ-Ilkiv. Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. Springer, 2014.
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Model Predictive Vibration Control Efficient Constrained Mpc Vibration Control For Lightly Damped Mechanical Structures. Springer, 2012.
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Khaled, Nassim, and Bibin Pattel. Practical Design and Application of Model Predictive Control: MPC for MATLAB® and Simulink® Users. Elsevier Science & Technology Books, 2018.
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Vaez-Zadeh, Sadegh. Predictive, Deadbeat, and Combined Controls. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0005.
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In this chapter, three control methods recently developed for or applied to electric motors in general and to permanent magnet synchronous (PMS) motors, in particular, are presented. The methods include model predictive control (MPC), deadbeat control (DBC), and combined vector and direct torque control (CC). The fundamental principles of the methods are explained, the machine models appropriate to the methods are derived, and the control systems are explained. The PMS motor performances under the control systems are also investigated. It is elaborated that MPC is capable of controlling the motor under an optimal performance according to a defined objective function. DBC, on the other hand, provides a very fast response in a single operating cycle. Finally, combined control produces motor dynamics faster than one under VC, with a smoother performance than the one under DTC.
Book chapters on the topic "Model predictive controller (MPC)"
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Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.
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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC controllers) and high efficiency. Our experimental results demonstrate the sophisticated nature of the distributed controllers we learn. In particular, the neural controllers are capable of achieving myriad flocking-oriented control objectives, including flocking formation, collision avoidance, obstacle avoidance, predator avoidance, and target seeking. Moreover, they generalize the behavior seen in the training data to achieve these objectives in a significantly broader range of scenarios. In terms of verification of our neural flocking controller, we use a form of statistical model checking to compute confidence intervals for its convergence rate and time to convergence.
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Klaučo, Martin, and Michal Kvasnica. "Inner Loops with Model Predictive Control Controllers." In MPC-Based Reference Governors. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17405-7_6.
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Sharma, Veena, Vineet Kumar, R. Naresh, and V. Kumar. "MPA Optimized Model Predictive Controller for Optimal Control of an AVR System." In Intelligent Data Engineering and Analytics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7524-0_6.
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Camacho, Eduardo F., and Carlos Bordons. "Multivariable MPC." In Model Predictive Control. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_6.
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Camacho, Eduardo F., and Carlos Bordons. "Constrained MPC." In Model Predictive Control. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_7.
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Camacho, Eduardo F., and Carlos Bordons. "Robust MPC." In Model Predictive Control. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_8.
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Jonath, Lucas, Jörg Luderich, Jonas Brezina, Ana Maria Gonzalez Degetau, and Selim Karaoglu. "Improving the Thermal Behavior of High-Speed Spindles Through the Use of an Active Controlled Heat Pipe System." In Lecture Notes in Production Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_16.
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AbstractThe thermo-elastic behavior of high-speed spindles has a significant influence on the machine accuracy. The Tool Center Point (TCP) changes continuously, not only due to the different temperature levels and energy inputs during warm-up, full-load and part-load operation, but also during interruptions for workpiece or tool changes. In this paper a heat pipe based tempering system is presented to control the spindle temperature and thus to keep the TCP displacement at a constant level, regardless of speed and load. As effective passive heat transfer components, heat pipes can be used not only to cool the system but also to insert heat into it. This capability of reversing the heat flow enables a high controllability of the temperature field in a bidirectional way and allows innovative capabilities of using advanced control algorithms. This paper describes the overall heat pipe concept and focuses on its potential as a key element for dynamic temperature control systems. Experimental results prove the feasibility of the concept with a simple on-off controller, achieving the reduction of the TCP displacement variation of a 2.2 kW spindle by 62% of its original value. The potential of the tempering concept forms the base for the deployment of various advanced control systems, such as Model-based Predictive Control (MPC), Fuzzy or Reinforcement Learning.
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Klaučo, Martin, and Michal Kvasnica. "Model Predictive Control." In MPC-Based Reference Governors. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17405-7_3.
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Kouvaritakis, Basil, and Mark Cannon. "Introduction to Stochastic MPC." In Model Predictive Control. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24853-0_6.
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Takács, Gergely, and Boris Rohal’-Ilkiv. "Basic MPC Formulation." In Model Predictive Vibration Control. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2333-0_6.
Full textConference papers on the topic "Model predictive controller (MPC)"
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Piper, Matthew, Pranav Bhounsule, and Krystel K. Castillo-Villar. "How to Beat Flappy Bird: A Mixed-Integer Model Predictive Control Approach." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5285.
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Flappy Bird is a mobile game that involves tapping the screen to navigate a bird through a gap between pairs of vertical pipes. When the bird passes through the gap, the score increments by one and the game ends when the bird hits the floor or a pipe. Surprisingly, Flappy Bird is a very difficult game and scores in single digits are not uncommon even after extensive practice. In this paper, we create three controllers to play the game autonomously. The controllers are: (1) a manually tuned controller that flaps the bird based on a vertical set point condition; (2) an optimization-based controller that plans and executes an optimal path between consecutive tubes; (3) a model-based predictive controller (MPC). Our results showed that on average, the optimization-based controller scored highest, followed closely by the MPC, while the manually tuned controller scored the least. A key insight was that choosing a planning horizon slightly beyond consecutive tubes was critical for achieving high scores. The average computation time per iteration for the MPC was half that of optimization-based controller but the worst case time (maximum time) per iteration for the MPC was thrice that of optimization-based controller. The success of the optimization-based controller was due to the intuitive tuning of the terminal position and velocity constraints while for the MPC the important parameters were the prediction and control horizon. The MPC was straightforward to tune compared to the other two controllers. Our conclusion is that MPC provides the best compromise between performance and computation speed without requiring elaborate tuning.
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Lenssen, Daan, Alberto Bertipaglia, Felipe Santafe, and Barys Shyrokau. "Combined Path Following and Vehicle Stability Control using Model Predictive Control." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0645.
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<div class="section abstract"><div class="htmlview paragraph">This paper presents an innovative combined control using Model Predictive Control (MPC) to enhance the stability of automated vehicles. It integrates path tracking and vehicle stability control into a single controller to satisfy both objectives. The stability enhancement is achieved by computing two expected yaw rates based on the steering wheel angle and on lateral acceleration into the MPC model. The vehicle's stability is determined by comparing the two reference yaw rates to the actual one. Thus, the MPC controller prioritises path tracking or vehicle stability by actively varying the cost function weights depending on the vehicle states. Using two industrial standard manoeuvres, i.e. moose test and double lane change, we demonstrate a significant improvement in path tracking and vehicle stability of the proposed MPC over eight benchmark controllers in the high-fidelity simulation environment. The numerous benchmark controllers use different path tracking and stability control methods to assess each performance benefit. They are split into two groups: the first one uses differential braking in the control output, while the second group can only provide an equal brake torque for the wheels in the same axle. Furthermore, the controller's robustness is evaluated by changing various parameters, e.g. initial vehicle speed, mass and road friction coefficient. The proposed controller keeps the vehicle stable at higher speeds even with varying conditions.</div></div>
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Parry, Adam, Brandon Hencey, and Jon Zumberge. "Model Predictive Control for a Synchronous Machine With a Pulsed, Constant-Power Load." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3110.
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Abstract In this paper, the problem of controlling synchronous machines driving high pulsed, constant-power loads (CPLs) with fast ramp rates is investigated. Using a PI controller to provide offset-free tracking of the generator voltage in steady state, we design controllers using Model Predictive Control which act as a reference governor to ensure power quality constraints are met during transients. However, it is shown that a standard linear MPC algorithm creates a steady state offset due to model mismatch at off-nominal power levels resulting in loss of power quality. This problem is corrected by creating multiple linear models of the generator dynamics linearized around the nominal and high power operating points. We then demonstrate that a Hybrid Model Predictive Control algorithm (using the constrained piecewise affine prediction model) exhibits zero offset during the high power pulse. The Hybrid MPC algorithm also keeps the generator voltage within the required constraints. This approach has the benefit of correcting the model mismatch issue without using a computationally expensive nonlinear Model Predictive Control algorithm. Future work will focus on implementing and testing this hybrid MPC controller on a generator via explicit MPC techniques.
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Vroemen, B. G., H. A. van Essen, A. A. van Steenhoven, and J. J. Kok. "Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation." In ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/98-gt-100.
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The feasibility of Model Predictive Control (MPC) applied to a laboratory gas turbine installation is investigated. MPC explicitly incorporates (input- and output-) constraints in its optimizations, which explains the choice for this computationally demanding control strategy. Strong nonlinearities, displayed by the gas turbine installation, cannot always be handled adequately by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combination of these. We implement these methods, using a nonlinear model of the installation, and compare them to linear MPC. It is shown that controller performance can be improved, without increasing controller execution-time excessively.
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Chen, Xiao, and Qian Wang. "A Data-Driven Thermal Sensation Model Based Predictive Controller for Indoor Thermal Comfort and Energy Optimization." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6131.
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This paper proposes a model predictive controller (MPC) using a data-driven thermal sensation model for indoor thermal comfort and energy optimization. The uniqueness of this empirical thermal sensation model lies in that it uses feedback from occupants (occupant actual votes) to improve the accuracy of model prediction. We evaluated the performance of our controller by comparing it with other MPC controllers developed using the Predicted Mean Vote (PMV) model as thermal comfort index. The simulation results demonstrate that in general our controller achieves a comparable level of energy consumption and comfort while eases the computation demand posed by using the PMV model in the MPC formulation. It is also worth pointing out that since we assume that our controller receives occupant feedback (votes) on thermal comfort, we do not need to monitor the parameters such as relative humidity, air velocity, mean radiant temperature and occupant clothing level changes which are necessary in the computation of PMV index. Furthermore simulations show that in cases where occupants’ actual sensation votes might deviate from the PMV predictions (i.e., a bias associated with PMV), our controller has the potential to outperform the PMV based MPC controller by providing a better indoor thermal comfort.
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Angatkina, Oyuna, and Andrew Alleyne. "Model Predictive Control of a Pumped Two-Phase Cooling System With Microchannel Heat Exchangers." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9143.
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Two-phase cooling systems provide a viable technology for high–heat flux rejection in electronic systems. They provide high cooling capacity and uniform surface temperature. However, a major restriction of their application is the critical heat flux condition (CHF). This work presents model predictive control (MPC) design for CHF avoidance in two-phase pump driven cooling systems. The system under study includes multiple microchannel heat exchangers in series. The MPC controller performance is compared to the performance of a baseline PI controller. Simulation results show that while both controllers are able to maintain the two-phase cooling system below CHF, MPC has significant reduction in power consumption compared to the baseline controller.
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Mazibuko, N., K. T. Akindeji, K. Moloi, and G. Sharma. "Improved Model Predictive controller (MPC) for an Automatic Voltage Regulator (AVR)." In 2024 32nd Southern African Universities Power Engineering Conference (SAUPEC). IEEE, 2024. http://dx.doi.org/10.1109/saupec60914.2024.10445066.
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Ren, Dejin, Wanli Lu, Jidong Lv, Lijun Zhang, and Bai Xue. "Model Predictive Control with Reach-avoid Analysis." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/604.
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In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. Existing model predictive control (MPC) methods learn from a set of discrete states visited by previous (sub-)optimized trajectories and thus result in computationally expensive mixed-integer nonlinear optimization. In this paper a novel MPC method is proposed based on reach-avoid analysis to solve the controller synthesis problem iteratively. The reach-avoid analysis is concerned with computing a reach-avoid set which is a set of initial states such that the system can reach the target set successfully. It not only provides terminal constraints, which ensure feasibility of MPC, but also expands discrete states in existing methods into a continuous set (i.e., reach-avoid sets) and thus leads to nonlinear optimization which is more computationally tractable online due to the absence of integer variables. Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples.
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Liu, Heng, Wei Sun, Hao Sun, Jianfeng Tao, and Chengliang Liu. "A Deep Koopman-Based Model Predictive Control Method for Valve-Controlled Hydraulic Cylinder Systems." In BATH/ASME 2022 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fpmc2022-89019.
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Abstract Hydraulic servo systems are widely applied in construction machinery due to their simple structure and strong bearing capacity. However, considering the nonlinearity and asymmetry in such systems, it is not easy to establish a precise discrete prediction model for the design of the MPC controller, which is a key factor affecting the precision of motion control. To address this issue, this paper proposes a deep Koopman-based model predictive control (MPC) method for valve-controlled asymmetric hydraulic cylinder (VCHC) systems. Significantly, a linear predictor is developed based on the ability of the Koopman operator to lift a nonlinear space to a linear space globally. The simulation results show that the MPC algorithm combined with the Deep Koopman operator has excellent control performance.
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Liu, Yilun, Lei Zuo, and Xiudong Tang. "Regenerative Vibration Control of Tall Buildings Using Model Predictive Control." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3988.
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The regenerative Tuned Mass Damper (TMD) can convert the vibration energy of the tall building into the electricity, by replacing the damping element with electromagnetic harvester. The energy harvesting circuit therein which can regulate the electricity and control the vibration will introduce some constraints when designing vibration controller. This paper designed the vibration controller based on Model Predictive Control (MPC). The control force constraints were taken into consideration before designing the controller. The building model with semi-active constraints due to the regenerative properties of the TMD is converted into a Mixed Logical Dynamical (MLD) system first. Then the optimal controller is designed by solving the Mixed Integer Quadratic Programming (MIQP) problem. The results were evaluated and compared to the ones using “clipped-optimal” controller with the same constraints. It is found that the MPC controller can provide the same or better vibration control Results depending on the predicted horizon. Besides, an explicit MPC is obtained to reduce the online computation effort.
Reports on the topic "Model predictive controller (MPC)"
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Yang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2240.
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California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered charging station. The objectives are to increase customers’ satisfaction, reduce the power grid burden, and maximize the profitability of charging stations using state-of-the-art global optimization techniques, machine-learning-based solar power prediction, and model predictive control (MPC). The proposed research has broad societal impacts and significant intellectual merits. First, it meets the demand for green transportation by increasing the number of EV users and reducing the transportation sector’s impacts on climate change. Second, an optimal scheduling tool enables fast charging of EVs and thus improves the mobility of passengers. Third, the designed planning tools enable an optimal design of charging stations equipped with a solar panel and battery energy storage system (BESS) to benefit nationwide transportation system development.
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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0741.
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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).