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Relevant bibliographies by topics / Fuzzy model based multivariable predictive control

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Academic literature on the topic 'Fuzzy model based multivariable predictive control'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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Journal articles on the topic "Fuzzy model based multivariable predictive control"

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Nibiret, Getinet Asimare, and Abrham Tadesse Kassie. "Fuzzy Model Based Model Predictive Control for Biomass Boiler." International Journal of Engineering Research in Africa 71 (September 18, 2024): 93–108. http://dx.doi.org/10.4028/p-6uv4x4.

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In the realm of renewable energy, biomass plays a crucial role. A key component of power plants, the biomass boiler unit, is responsible for steam production. This unit operates as a nonlinear, highly coupled multivariable process. Traditional controllers used in the industry are ineffective for such systems. To address this, this paper presents a novel approach: a model predictive controller designed for biomass boiler plants. Fuzzy modelling, employed to approximate nonlinear functions to linear ones, is used for system identification. The methodology is implemented using MATLAB/Simulink and the Fuzzy modelling and identification (FMID) toolbox, utilizing input-output data from the Wenji-Shoa sugar factory for fuzzy model identification. The proposed controller demonstrates significant improvements, achieving settling times of 7.5, 13, and 7 seconds, with acceptable overshoots of 0.5%, 0.39%, and 0.46% for pressure, temperature, and level, respectively, for MISO systems. In contrast, the MPC shows improved performance in MIMO systems compared to MISO systems, with settling times of 5, 4, and 7 seconds, while the overshoot is reduced only for the pressure output, with 0.214%.

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Preglej, Aleksander, Jakob Rehrl, Daniel Schwingshackl, Igor Steiner, Martin Horn, and Igor Škrjanc. "Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system." Energy and Buildings 82 (October 2014): 520–33. http://dx.doi.org/10.1016/j.enbuild.2014.07.042.

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Huaguang, Zhang, Lilong Cai, and Zeungnam Bien. "A multivariable generalized predictive control approach based on T–S fuzzy model." Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 9, no. 3-4 (2000): 169–89. https://doi.org/10.3233/ifs-2000-125.

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Jeronymo, Daniel Cavalcanti, and Antonio Augusto Rodrigues Coelho. "Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models." PLOS ONE 11, no. 9 (2016): e0163116. http://dx.doi.org/10.1371/journal.pone.0163116.

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Bououden, S., M. Chadli, S. Filali, and A. El Hajjaji. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach." Renewable Energy 37, no. 1 (2012): 434–39. http://dx.doi.org/10.1016/j.renene.2011.06.025.

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Che, Yinping, Zhonggai Zhao, Zhiguo Wang, and Fei Liu. "Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model." Journal of Process Control 119 (November 2022): 1–12. http://dx.doi.org/10.1016/j.jprocont.2022.09.005.

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Vallejo LLamas, Pedro M., and Pastora Vega. "Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes." Complexity 2019 (January 3, 2019): 1–29. http://dx.doi.org/10.1155/2019/5720185.

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A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.

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B., Babes, Hamouda N., Kahla S., Amar H., and S. M. Ghoneim S. "Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators." Electrical Engineering & Electromechanics, no. 3 (May 30, 2022): 51–62. https://doi.org/10.20998/2074-272X.2022.3.08.

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<strong><em>Introduction. </em></strong><em>A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. <strong>The novelty. </strong>The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose.</em> <em>Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement.</em> Methods. <em>In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.</em>

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Vallejo LLamas, Pedro M., and Pastora Vega. "Practical Computational Approach for the Stability Analysis of Fuzzy Model-Based Predictive Control of Substrate and Biomass in Activated Sludge Processes." Processes 9, no. 3 (2021): 531. http://dx.doi.org/10.3390/pr9030531.

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This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed.

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Babes, B., N. Hamouda, S. Kahla, H. Amar, and S. S. M. Ghoneim. "Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators." Electrical Engineering & Electromechanics, no. 3 (May 30, 2022): 51–62. http://dx.doi.org/10.20998/2074-272x.2022.3.08.

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Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.

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Dissertations / Theses on the topic "Fuzzy model based multivariable predictive control"

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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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Kandiah, Sivasothy. "Fuzzy model based predictive control of chemical processes." Thesis, University of Sheffield, 1996. http://etheses.whiterose.ac.uk/3029/.

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The past few years have witnessed a rapid growth in the use of fuzzy logic controllers for the control of processes which are complex and ill-defined. These control systems, inspired by the approximate reasoning capabilities of humans under conditions of uncertainty and imprecision, consist of linguistic 'if-then' rules which depend on fuzzy set theory for representation and evaluation using computers. Even though the fuzzy rules can be built from purely heuristic knowledge such as a human operator's control strategy, a number of difficulties face the designer of such systems. For any reasonably complex chemical process, the number of rules required to ensure adequate control in all operating regions may be extremely large. Eliciting all of these rules and ensuring their consistency and completeness can be a daunting task. An alternative to modelling the operator's response is to model the process and then to incorporate the process model into some sort of model-based control scheme. The concept of Model Based Predictive Control (MB PC) has been heralded as one of the most significant control developments in recent years. It is now widely used in the chemical and petrochemical industry and it continues to attract a considerable amount of research. Its popularity can be attributed to its many remarkable features and its open methodology. The wide range of choice of model structures, prediction horizon and optimisation criteria allows the control designer to easily tailor MBPC to his application. Features sought from such controllers include better performance, ease of tuning, greater robustness, ability to handle process constraints, dead time compensation and the ability to control nonminimum phase and open loop unstable processes. The concept of MBPC is not restricted to single-input single-output (SISO) processes. Feedforward action can be introduced easily for compensation of measurable disturbances and the use of state-space model formulation allows the approach to be generalised easily to multi-input multi-output (MIMO) systems. Although many different MBPC schemes have emerged, linear process models derived from input-output data are often used either explicitly to predict future process behaviour and/or implicitly to calculate the control action even though many chemical processes exhibit nonlinear process behaviour. It is well-recognised that the inherent nonlinearity of many chemical processes presents a challenging control problem, especially where quality and/or economic performance are important demands. In this thesis, MBPC is incorporated into a nonlinear fuzzy modelling framework. Even though a control algorithm based on a 1-step ahead predictive control strategy has initially been examined, subsequent studies focus on determining the optimal controller output using a long-range predictive control strategy. The fuzzy modelling method proposed by Takagi and Sugeno has been used throughout the thesis. This modelling method uses fuzzy inference to combine the outputs of a number of auto-regressive linear sub-models to construct an overall nonlinear process model. The method provides a more compact model (hence requiring less computations) than fuzzy modelling methods using relational arrays. It also provides an improvement in modelling accuracy and effectively overcomes the problems arising from incomplete models that characterise relational fuzzy models. Difficulties in using traditional cost function and optimisation techniques with fuzzy models have led other researchers to use numerical search techniques for determining the controller output. The emphasis in this thesis has been on computationally efficient analytically derived control algorithms. The performance of the proposed control system is examined using simulations of the liquid level in a tank, a continuous stirred tank reactor (CSTR) system, a binary distillation column and a forced circulation evaporator system. The results demonstrate the ability of the proposed system to outperform more traditional control systems. The results also show that inspite of the greatly reduced computational requirement of our proposed controller, it is possible to equal or better the performance of some of the other fuzzy model based control systems that have been proposed in the literature. It is also shown in this thesis that the proposed control algorithm can be easily extended to address the requirements of time-varying processes and processes requiring compensation for disturbance inputs and dead times. The application of the control system to multivariable processes and the ability to incorporate explicit constraints in the optimisation process are also demonstrated.

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Alvarado, Christiam Segundo Morales. "Estudo e implementação de métodos de validação de modelos matemáticos aplicados no desenvolvimento de sistemas de controle de processos industriais." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-05092017-092437/.

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A validação de modelos lineares é uma etapa importante em um projeto de Identificação de Sistemas, pois a escolha correta do modelo para representar a maior parte da dinâmica do processo, dentro de um número finito de técnicas de identificação e em torno de um ponto de operação, permite o sucesso no desenvolvimento de controladores preditivos e de controladores robustos. Por tal razão, o objetivo principal desta Tese é o desenvolvimento de um método de validação de modelos lineares, tendo como ferramentas de avaliação os métodos estatísticos, avaliações dinâmicas e análise da robustez do modelo. O componente principal do sistema de validação de modelos lineares proposto é o desenvolvimento de um sistema fuzzy para análise dos resultados obtidos pelas ferramentas utilizadas na etapa de validação. O projeto de Identificação de Sistemas é baseado em dados reais de operação de uma Planta-Piloto de Neutralização de pH, localizada no Laboratório de Controle de Processos Industriais da Escola Politécnica da USP. Para verificar o resultado da validação, todos os modelos são testados em um controlador preditivo do tipo QDMC (Quadratic Dynamic Matrix Control) para seguir uma trajetória de referência. Os critérios utilizados para avaliar o desempenho do controlador QDMC, para cada modelo utilizado, foram a velocidade de resposta do controlador e o índice da mínima variabilidade da variável de processo. Os resultados mostram que a confiabilidade do sistema de validação projetado para malhas com baixa e alta não-linearidade em um processo real, foram de 85,71% e 50%, respectivamente, com relação aos índices de desempenho obtidos pelo controlador QDMC.<br>Linear model validation is the most important stage in System Identification Project because, the model correct selection to represent the most of process dynamic allows the success in the development of predictive and robust controllers, within identification technique finite number and around the operation point. For this reason, the development of linear model validation methods is the main objective in this Thesis, taking as a tools of assessing the statistical, dynamic and robustness methods. Fuzzy system is the main component of model linear validation system proposed to analyze the results obtained by the tools used in validation stage. System Identification project is performed through operation real data of a pH neutralization pilot plant, located at the Industrial Process Control Laboratory, IPCL, of the Escola Politécnica of the University of São Paulo, Brazil. In order to verify the validation results, all modes are used in QDMC type predictive controller, to follow a set point tracking. The criterions used to assess the QDMC controller performance were the speed response and the process variable minimum variance index, for each model used. The results show that the validation system reliability were 85.71% and 50% projected for low and high non-linearity in a real process, respectively, linking to the performance indexes obtained by the QDMC controller.

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Franco, Ivan Carlos 1976. "Controle preditivo baseado em modelo neuro-fuzzy de sistemas não-lineares aplicado em sistema de refrigeração = Model predictive control based on neuro-fuzzy nonlinear systems applied to a refrigeration plant." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266817.

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Orientador: Flávio Vasconcelos da Silva<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química<br>Made available in DSpace on 2018-08-20T15:14:24Z (GMT). No. of bitstreams: 1 Franco_IvanCarlos_D.pdf: 30656846 bytes, checksum: 73b0216f94ce7393fb51d78cc6e4ea7f (MD5) Previous issue date: 2012<br>Resumo: Os sistemas de refrigeração estão presentes em diferentes ramos da indústria e caracterizam-se como grandes consumidores de energia com considerável comportamento não-linear. Inúmeros trabalhos vêm sido desenvolvidos para promover a redução dos gastos energéticos e a minimização dos efeitos das não-linearidades nestes sistemas. A aplicação da automação e do controle de processos, particularmente o uso de técnicas avançadas de controle, são estratégias amplamente utilizadas para esta finalidade. O Controle Preditivo baseado em Modelos (MPC) é capaz de estabilizar processos onde há não-linearidades, sendo promissora a sua aplicação em sistemas de refrigeração. Neste trabalho, foi desenvolvido um SIStema de MOnitoramento e Controle Avançado para Refrigeração (SISMOCAR) capaz de monitorar, em tempo real, através da comunicação OPC (OLE for Process Control), todas as variáveis envolvidas no ciclo de refrigeração e também realizar o controle das variáveis de interesse. Modelos Takagi-Sugeno (SISO) para a predição das temperaturas de evaporação (Te) e do fluido secundário (Tp) foram desenvolvidos e validados, a partir da técnica ANFIS (Adaptative Network based Fuzzy Inference Systems), com análise de desempenho baseado no cálculo do VAF (Variance accounted for). Os modelos Takagi-Sugeno validados foram utilizados como base para Controladores Preditivos, mais especificamente um Controlador Preditivo Generalizado (GPC). Os controladores GPC's foram desenvolvidos sem restrições na função objetivo da ação do controlador. Foram projetados diferentes controladores preditivos para diferentes regras locais (Regras Fuzzy), sendo a ação global do controlador a integração ponderada dos modelos locais. Foram desenvolvidos três diferentes controladores: GPC1 (controle da temperatura de evaporação utilizando modelo de predição da Te em função da frequência do compressor); GPC2 (controle da temperatura do propilenoglicol utilizando modelo de predição da Tp em função da frequência do compressor) e GPC3 (controle da temperatura do propilenoglicol utilizando o modelo de predição da Tp em função da frequência da bomba do evaporador). Os testes realizados para rastreamento do set-point (±1 °C), com carga térmica constante de 3000 W, mostraram-se satisfatórios sendo os melhores desempenhos apresentados pelos controladores GPC1 e GPC2 onde o desvio da variável controlada em relação ao set point, dos respectivos controladores, ficou em torno de ± 0,3 °C<br>Abstract: Refrigeration systems can be found in many different branches of industry and are characterized as great energy consumers with considerable non-linear behavior. Several studies have been developed to promote the reduction of energy costs and to minimize the effects of nonlinearities in these systems. The use of automation and process control, particularly the use of advanced control techniques, is a widely used strategy for this purpose. The Model Predictive Control (MPC) is capable of stabilizing processes in which there are nonlinearities, and it is a promising application in refrigeration systems. In this work, a System for Monitoring and Advanced Control in Refrigeration (SISMOCAR) was developed using OPC (OLE for Process Control) communication. This feature allowed beyond real time monitoring for all variables involved in the refrigeration cycle, the control of the relevant variables. Furthermore, to predict the evaporating (Te) and the secondary fluid (Tp) temperatures, Takagi-Sugeno models (SISO) were developed and validated using the ANFIS (Adaptive Network-based Fuzzy Inference Systems) technique, with performance analysis based on the VAF (Variance accounted for) calculation. The validated Takagi-Sugeno models were used as basis for Predictive Controllers, specifically using Generalized Predictive Controller (GPC) strategy. The GPC controllers were developed without constraints in the objective function of the controller action. Different predictive controllers were designed for different local rules (Fuzzy Rules), being the weighted integration of the local models the controller global action. Three different controllers were developed: GPC1 (evaporating temperature control using the Te predictive model as a function of compressor frequency); GPC2 (control of propylene glycol temperature using the Tp predictive model as a function of compressor frequency) and GPC3 (control of propylene glycol temperature using the Tp predictive model as a function of the frequency of the evaporator pump). The tests performed for the set-point tracking (± 1 °C), with constant thermal load of 3000 W, were considered satisfactory and best performances were those obtained by GPC1 and GPC2 controllers, in which the controlled variable was around ± 0,3 °C<br>Doutorado<br>Sistemas de Processos Quimicos e Informatica<br>Doutor em Engenharia Química

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Lima, Nádson Murilo Nascimento. "Modelagem e controle hibrido preditivo por logica fuzzy de processos de polimerização." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266795.

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Orientador: Rubens Maciel Filho<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica<br>Made available in DSpace on 2018-08-09T11:12:44Z (GMT). No. of bitstreams: 1 Lima_NadsonMuriloNascimento_M.pdf: 1887705 bytes, checksum: e0761ce0e30dd05a47c9cfbe9b0968ef (MD5) Previous issue date: 2006<br>Resumo: A síntese de controladores representa uma importante vertente dos desenvolvimentos atuais no campo da pesquisa acadêmica e industrial. Um controlador bem projetado pode significar sucesso no que se refere aos objetivos de produção, sendo gerados materiais com as especificações desejadas e proporcionando que o sistema opere sob certas restrições, levando em consideração aspectos relativos à operabilidade, segurança e minimização de resíduos. Para tanto, sabe-se que as etapas de modelagem são fundamentais para a delineação de estratégias de controle. No entanto, a obtenção de representações matemáticas precisas e, ao mesmo tempo, aplicáveis para controle da maioria dos processos de interesse da engenharia química é uma tarefa árdua, devido à presença de comportamentos dinâmicos não lineares e variantes ao longo do espaço e do tempo. Deste modo, busca-se a obtenção de modelos mais simples, porém dotados da imprescindível representatividade inerente aos sistemas de produção, a fim de serem projetadas estruturas de controle adequadas para cada necessidade específica. Este trabalho enfoca o desenvolvimento de um controlador híbrido preditivo baseado em modelos nebulosos (fuzzy) tipo Takagi-Sugeno para processos de polimerização, os quais apresentam dinâmicas altamente complexas e de difícil modelagem matemática, dificultando assim a aplicação de metodologias convencionais de controle. Foram considerados dois casos de estudo para análise de desempenho do controlador proposto: o processo de copolimerização em solução do metacrilato de metila e acetato de vinila, e a copolimerização industrial do eteno/1-buteno com catalisador Ziegler-Natta solúvel. Os modelos fenomenológicos de ambos os processos já se encontram descritos na literatura, sendo considerados como plantas virtuais para geração de dados dinâmicos e implementação do controlador. A partir de simulações computacionais, os modelos dinâmicos nebulosos funcionais foram construídos ¿ os quais demonstraram excelentes capacidades para predição das saídas dos processos como uma função dos dados dinâmicos de entrada ¿ sendo, posteriormente, inseridos na estrutura interna do controle preditivo DMC (Dynamic Matrix Control). A escolha do controlador DMC como base para o desenvolvimento da estrutura proposta deve-se ao fato de sua notória aplicabilidade industrial, aliada à simplicidade de projeto e execução, além de possibilitar a incorporação de restrições nas variáveis controladas e manipuladas. Por fim, foram comparados os desempenhos entre os controladores híbrido e DMC convencional para os problemas regulatório e servo, fornecendo resultados satisfatórios em ambas as situações. Isto demonstra o alto potencial do algoritmo proposto para o controle de sistemas não lineares<br>Abstract: Controller synthesis represents an important slope of recent development in the field of academic and industry research. A well-projected controller may express the success in reference to the aim of production, and it also creates materials with desirable specification and it allows that the system operates under certain restrictions, considering aspects related to operability, safety and minimization of residue. Then, it¿s known that modeling stage is fundamental to delineation of controller strategies. However, the obtaining of precise and applicable mathematical representation to control most of relevant process in chemist engineering is an arduous task, because of the presence of nonlinear dynamic behavior and space-time variation. Therefore, it searches obtaining of simplier models, but gifted of essential representativity inherent to production system, because of this suitable control structure to each specific necessity projected. This work focus on the development of predictive hybrid controller based on fuzzy models, type Takagi-Sugeno, to process of copolymerization, that present high complex dynamic and hard mathematical modeling, what is also difficult to apply conventional methodologies of control. Two study cases that present analyze of purpose controller were considered: the process of copolymerization in solution of methyl methacrylate and vinyl acetate, and industry copolymerization of ethene/1-butene with Ziegler-Natta catalyzation. The phenomenologic models of the two processes have already described in relevant literature, and they are considered as virtual plants to create dynamic data and controller implementation. Based on computer simulation, functional dynamic fuzzy models were made ¿ they demonstrate excellent capacity to output prediction in process as a function of input dynamic data ¿ and they are, subsequently, inserted in an internal structure of DMC (Dynamic Matrix Control) predictive control. The choice of DMC controller as the base to development of proposal structure is responsible to the fact of its well-known industry applicability, allied to simplicity of the project and execution, beyond it makes possible the incorporation of restrictions in controlled and manipulated variables. Finally, the performance between hybrid controller and conventional DMC to regulatory and servo problems were compared, and it supplies satisfactory results in both situation. It demonstrates high potential of propose algorithm to control nonlinear system<br>Mestrado<br>Desenvolvimento de Processos Químicos<br>Mestre em Engenharia Química

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Dias, Rafael Nunes Hidalgo Monteiro. "Análise comparativa de técnicas de controle Fuzzy e matriz dinâmica aplicadas à máquina de corrente contínua." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/8098.

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Submitted by Erika Demachki (erikademachki@gmail.com) on 2018-01-10T15:49:59Z No. of bitstreams: 2 Dissertação - Rafael Nunes Hidalgo Monteiro Dias - 2017.pdf: 14690145 bytes, checksum: d12e70a2cd0ee1087a184468c55f0b08 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-01-11T11:02:36Z (GMT) No. of bitstreams: 2 Dissertação - Rafael Nunes Hidalgo Monteiro Dias - 2017.pdf: 14690145 bytes, checksum: d12e70a2cd0ee1087a184468c55f0b08 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Made available in DSpace on 2018-01-11T11:02:36Z (GMT). No. of bitstreams: 2 Dissertação - Rafael Nunes Hidalgo Monteiro Dias - 2017.pdf: 14690145 bytes, checksum: d12e70a2cd0ee1087a184468c55f0b08 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-11-27<br>This work presents a comparison between Fuzzy and dynamic matrix controllers. These controllers are applied to the direct current (DC) motor speed control, triggered by fully controlled three-phase rectifier. The construction of the real system and the development and validation of the computational model are described. The controllers’ parameters are obtained through an optimization process. Both control techniques are compared and results indicate better performance of the optimized controllers, which suggest their promise in nonlinear systems’ control, in which seeks out control without error, that fulfills well its duty and its able to resist the fatigues.<br>Este trabalho apresenta o comparativo entre os controladores Fuzzy e matriz dinâmica. Estes controladores são aplicados ao controle de velocidade do motor de corrente contínua, acionado por retificador trifásico totalmente controlado. A metodologia parte da construção do sistema real e do desenvolvimento e validação do modelo computacional. A obtenção dos parâmetros dos controladores é realizada através do processo de otimização. Realiza-se análise comparativa entre as técnicas de controle e os resultados apontam para a proeminência de controladores sintonizados via processo de otimização como técnica promissora a ser empregada em controle de sistemas não lineares, nos quais buscam-se controle em que não há erro, que cumpra bem o seu dever e apto para resistir às fadigas.

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Roy, Prodyut Kumer. "Model predictive control of a multivariable soil heating process /." 2005.

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Resmi, Ilmiyah Elrosa Citra, and Ilmiyah Elrosa Citra Resmi. "Nonlinear Model Predictive Control for Nonlinear Systems Based on a Takagi-Sugeno Fuzzy Model." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/64581695824199988961.

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碩士<br>國立臺灣科技大學<br>自動化及控制研究所<br>103<br>In nonlinear systems, stability is one of the problems that have to be solved to make the system controlled. This thesis introduces the alternative approach to stabilize system by combining the T-S Fuzzy model and the finite-horizon Model Predictive Control (MPC). This research constructs the MPC based on Laguerre functions in the T-S models and proves the existence of the Lyapunov function. By applying this method to the system, an Inverted Pendulum on cart system, the output system shows a good performance. This controller can bring system back to the origin and reject the disturbance from inside and outside the system. The designed controller can hold the parameters of the plant alteration. From any alterations the output system can return to the origin and also reject the disturbance.

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CHANG, YU-HENG, and 張堉恆. "Adaptive Model Predictive Control Based on Takagi-Sugeno Fuzzy Model for Floating Hydropower System Design." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/v28n93.

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碩士<br>逢甲大學<br>自動控制工程學系<br>107<br>Owing to the increasing environmental problems of fossil fuel consumption and global warming, the power generation systems of renewable energy have received increasing attention in recent years. In renewable energy, the proportion of hydropower is rapidly growing into a mainstream and competing with traditional energy power generation. The storage capacity of hydropower energy has grown exponentially to become a cornerstone of renewable energy. Because hydropower involves a wide range of science and technology, it includes fluid dynamics, power electronic converters, and power systems, and the main techniques of hydropower are consisted of generators theory. There are safe and good performance of the hydrokinetic energy conversion system (HECS) that are also subject to strict grid requirements. Moreover, Taiwan is surrounded by the sea and there is passed by Kuroshio every year. Therefore, the development of a floating hydropower system is quite suitable for independent power generation program in Taiwan. However, the Kuroshio floating power generation system faces the large water depth environment and unstable ocean current characteristics. Achieving stable voltage and maximum power output are the keys to the development in this thesis. The thesis is proposed adaptive model predictive control based on Takagi-Sugeno fuzzy model for floating hydropower system design. It’s mainly designed for the permanent magnet synchronous generator in the floating hydropower system, so that the generator can maintain a constant speed and isn’t affected by the three-phase current imbalance. Eventually, the output voltage of the hydropower system remains stable while promoting maximum power efficiency. Firstly, the current flow rate of the random current detected by the current meter, and the maximum angular tracking method is used to calculate the angular velocity of the permanent magnet synchronous generator. After the external angular velocity reference signal is compared with internal angular velocity to obtain the tracking error, the torque reference signal is acquired through the proportional-integral (PI) controller, and the magnetic flux reference signal is obtained by the maximum torque per ampere. Furthermore, due to the increasing research on speed-free sensor encoders in recent years, this thesis designs the conditions of power generation systems in speed-free sensor encoders, using the model reference adaptive system method. The current signal inside the generator establishes the angular velocity signal, and the floating power generation system has reduced power generation efficiency due to the instability of the current. Therefore, the adaptive Takagi-Sugeno fuzzy model is used to approximate the nonlinear permanent magnet synchronization. The generator can efficiently approximate the nonlinear permanent magnet synchronous generator system, so that the adaptive Takagi-Sugeno fuzzy model system with linear affine and weight can be obtained by adaptive law online update to obtain the predictive control scheme. The stator current sequence of the shaft. In addition, we can estimate the electric torque and magnetic flux based on the estimated stator currents, and use the model predictive control method to make the three-phase permanent magnet synchronous motor provide stable torque and magnetic flux to estimate the best. Pulse-width-modulation (PWM) control commands to achieve a stable power supply to the floating ocean current power turbine. Finally, the experimental results of Matlab simulation show that the method of the PI controller is 20% more efficient than the PI method, and the energy can be stored with cost down and fast speed, so the output voltage is stable and maximum power efficiency at the same time.

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Tsai, Ju-Pien, and 蔡秉儒. "LMI-Based Fuzzy Model Predictive Control for Nonlinear Systems-Using NeuralNetworks to Update Grade Functions." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/17972160811849544037.

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碩士<br>中原大學<br>電機工程研究所<br>94<br>Model predictive control (MPC) is also known as receding horizon control (RHC) or moving horizon control (MHC). It is the most popular industrial control strategy, based on the idea of optimizing an objective function at each sampling. Although, many physical models are nonlinear, most researches on this issue are limited to linear systems. Recently, the Takagi-Sugeno (T-S) fuzzy approach has been used to model nonlinear systems using the decomposition of a nonlinear system into a set of linear subsystems. In this thesis, we will combine the T-S fuzzy model with the MPC strategy to deal with nonlinear systems. Since the grade functions of the fuzzy controller are independent to the system, in our control design we will update the grade functions via neural networks to achieve the better system performance. In addition, we will discuss the output tracking control based on output feedback design. To this end, the new concept, virtual- desired-variable (VDV) synthesis will be presented. The advantage of using the VDV synthesis is fully illustrated when we consider the example of the truck-trailer system. Although the system is only with a single input, we can control the different outputs via a unified manner. Therefore, we can switch the desired output arbitrarily without changing the control structure. Finally, observer-based control design is proposed to cope with the immeasurable state variables. For the most parts we focus on a common feature held by many physical systems where their membership functions of fuzzy sets satisfy a Lipschitz-like property. Based on this setting, control gains and observer gains can be designed separately. Two different types of systems, H'enon map and truck-trailer systems are considered to demonstrate the design procedure using satisfactory numerical simulation results.

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Book chapters on the topic "Fuzzy model based multivariable predictive control"

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Batur, C., C. C. Chan, and A. Srinivasan. "Fuzzy Model Based Predictive Controller." In Methods and Applications of Intelligent Control. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5498-7_6.

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Babuška, R., J. M. Sousa, and H. B. Verbruggen. "Inverse Fuzzy Model Based Predictive Control." In Advances in Fuzzy Control. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-7908-1886-4_6.

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Škrjanc, Igor, Katarina Kavšek-Biasizzo, and Drago Matko. "Predictive Control Based on a Fuzzy Model." In Advances in Fuzzy Control. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-7908-1886-4_13.

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Tang, Weiqiang, Yongda Qi, Mengke Guo, and Haiyan Gao. "Model Correction-Based Multivariable Predictive Functional Control for Uncertain Nonlinear Systems." In Nonlinear Dynamics and Control. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34747-5_27.

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dos Santos Coelho, Leandro, and Antonio Augusto Rodrigues Coelho. "Multivariable Predictive Control Based on Neural Network Model and Simplex-Evolutionary Hybrid Optimization." In Soft Computing in Industrial Applications. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0509-1_37.

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Nath, Ujjwal Manikya, Chanchal Dey, and Rajani K. Mudi. "Controlling of Twin Rotor MIMO System (TRMS) based on Multivariable Model Predictive Control." In Nanoelectronics, Circuits and Communication Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7486-3_44.

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Escaño, Juan Manuel, Kritchai Witheephanich, and Carlos Bordons. "Fuzzy Model Based Predictive Control of Reaction Temperature in a Pilot Plant." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66824-6_1.

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Li, Qinsheng, Li Jia, and Tian Yang. "Batch-Wise Updating Neuro-Fuzzy Model Based Predictive Control for Batch Processes." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45261-5_4.

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Zhang, Jun, Changzhu Zhang, Zhuping Wang, and Hao Zhang. "Fuzzy-Model-Based Robust Predictive Control for Path Tracking in Autonomous Driving." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_684.

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Stržinar, Žiga, and Igor Škrjanc. "Self-tuned Model-Based Predictive Control Using Evolving Fuzzy Model of a Non-linear Dynamic Process." In Explainable AI and Other Applications of Fuzzy Techniques. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82099-2_37.

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Conference papers on the topic "Fuzzy model based multivariable predictive control"

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Leng, Songtao, Zengwen Li, and Jianchun Jiang. "Vehicle Formation Control Based on Fuzzy Model Predictive Control." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695439.

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Wang, Jiale. "Research on Multivariable Control of Boilers Based on Model Predictive Control (MPC) and Genetic Algorithm." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020546.

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Yang, Zijian, Shuanghe Yu, Menghan Jiang, Yan Yan, and Ying Zhao. "Lyapunov-Based Distributed Model Predictive Control for Trajectory Tracking of USV with Fuzzy Constraints." In 2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON). IEEE, 2024. https://doi.org/10.1109/oncon62778.2024.10931548.

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Pavan Kumar, Y. V., S. N. V. Bramareswara Rao, and Gogulamudi Pradeep Reddy. "Fuzzy Logic Based Model Predictive Current Control for Improved Power Quality in Microgrid Clusters." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902675.

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Mahfouf, M., M. F. Abbod, and D. A. Linkens. "Multivariable adaptive fuzzy TSK model-based predictive control with feedforward." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076298.

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Ahmed, Sevil, Michail Petrov, and Alexandar Ichtev. "Fuzzy Model-Based Predictive Control applied to multivariable level control of multi tank system." In 2010 5th IEEE International Conference Intelligent Systems (IS). IEEE, 2010. http://dx.doi.org/10.1109/is.2010.5548359.

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Wang, Xudong, and Yiguo Li. "Model Predictive Control Based on Multivariable Disturbance Observer." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9727619.

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Raney, Anthony D., and Kelvin T. Erickson. "Stability Analysis of a Multivariable Model-Based Predictive Controller." In 1990 American Control Conference. IEEE, 1990. http://dx.doi.org/10.23919/acc.1990.4791213.

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Alamdari, Bahareh Vatankhah, Alireza Fatehi, and Ali Khaki-Sedigh. "Neural network model-based predictive control for multivariable nonlinear systems." In Control (MSC). IEEE, 2010. http://dx.doi.org/10.1109/cca.2010.5611265.

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Qian, Jixin, Yang Jianfeng, Zhao Jun, and Niu Jian. "Neural network model based predictive control for multivariable nonlinear systems." In International Conference on Intelligent Systems and Knowledge Engineering 2007. Atlantis Press, 2007. http://dx.doi.org/10.2991/iske.2007.101.

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