Fuzzy control system review

Overall intelligent control system which runs on fuzzy, genetic and neural algorithm is a promising engine for large –scale development of control systems . Its development relies on creating environments where anthropomorphic tasks can be performed autonomously or proactively with a human operator. Certainly, the ability to control processes with a degree of autonomy is depended on the quality of an intelligent control system envisioned. In this paper, a summary of published techniques for intelligent fuzzy control system is presented to enable a design engineer choose architecture for his particular purpose. Published concepts are grouped according to their functionality. Their respective performances are compared. The various fuzzy techniques are analyzed in terms of their complexity, efficiency, flexibility, start-up behavior and utilization of the controller with reference to an optimum control system condition

Anomaly detection for resilient control systems using fuzzy-neural data fusion engine

Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting

Modeling and predictive control with defocusing in thermosolar systems

Tese (dissertação) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2023.Uma das fontes renováveis de energia com maior potencial é a irradiação solar, que fornece uma quantidade significativa de energia à superfície da Terra. Devido à baixa densidade associados a esta fonte de energia, a sua exploração envolve a utilização de concentradores da irradiância solar, associados em série e paralelo, formando campos de coletores solares qu aquecem um fluido de trabalho. Devido ao ciclo diário de variação da intensidade da irradiância solar, estas plantas são dimensionadas de forma a captar múltiplas vezes a energia demandada, o que comumente gera situações de superaquecimento, que devem ser evitadas. Para evitar este superaquecimento, é possível reduzir o nível de concentração da energia solar nos coletores, em um processo denominado desfoque. Este desfoque geralmente não é incorporado no sistema de controle da planta, o que pode reduzir a eficiência global do processo tendo em vista que sistemas de segurança tendem a ser conservadores. Existem poucos trabalhos na literatura que exploram a incorporação do desfoque ativamente no controle do campo solar, sendo que os trabalhos existentes não contemplam como implementar este desfoque em coletores do tipo Fresnel. Este trabalho se dedica a começar a preencher esta lacuna na literatura, fornecendo propostas de estruturas de controle de campos solares com coletores Fresnel que incorporam o desfoque como variável manipulada, e também analisando os impactos da introdução desta atuação nas incertezas dos modelos utilizados no controle. Para este fim, foram consideradas técnicas avançadas de controle, como o Controle Preditivo Baseado em Modelo (MPC) que é muito utilizado em pesquisas visando aumentar a viabilidade deste tipo de processo. Este trabalho visa estudar técnicas de controle preditivo em sistemas termossolares com coletores fresnel, em especial MPC híbrido e não linear. Estes controladores são utilizados em problemas que envolvem todo o processo de geração de energia e as interações entre seus componentes, além de considerar o desfoque dos coletores utilizados para captar energia solar. As propostas de controladores apresentadas são desenvolvidas objetivando um custo computacional que permita a implementação em processos reais, além de promover melhoras em indicadores de produtividade do processo. Esta tese está dividida em sete capítulos, sendo estes: Uma introdução à motivação desta tese, bem como apresentação dos objetivos do trabalho; Uma revisão de aspectos relevantes da literatura pertentes aos estudos realizados nesta tese, como modelagem de plantas termossolares, Controle Preditivo Baseado em Modelo, modelos óticos de coletores fresnel e inteligência artificial; O primeiro capítulo de contribuições, contendo um estudo comparativo entre duas propostas de controladores já existentes na literatura com uma nova proposta de controlador, que incorpora aspectos das duas anteriores. Os controladores são avaliados em simulações de casos de operação do campo solar, onde se notou que cada uma das três propostas de controle é capaz de controlar o processo estudado não havendo uma proposta com desempenho claramente superior em todos os aspectos analisados; O segundo capítulo de contribuições, no qual é apresentada uma modelagem ótica simplificada de um coletor Fresnel, possibilitando a compreensão de como as características óticas deste tipo de coletor afetam o processo. Além disso, também é mostrado um estudo de possibilidades para o rastreamento do Sol neste tipo de coletor, avaliando os possíveis ganhos que a manipulação individual de cada espelho do coletor pode trazer para o processo. O modelo ótico foi validado com dados de um software de referência e com dados do fabricante do coletor modelado, apresentando proximidade com os dados de referência. As estratégias de seguimento do Sol consideradas apresentaram desempenho muito similar entre si, levando à conclusão de que a estratégia de implementação mais simples é a mais adequada. O terceiro capítulo de contribuições, onde mais uma proposta de controle preditivo que considera o desfoque como variável manipulada, mas desta vez utilizando um controlador preditivo baseado apenas em um modelo não-linear. Este controlador proposto foi comparado com um dos controladores já presentes na literatura e apresentou desempenho em geral superior, apesar de também ter custo computacional mais elevado. Neste capítulo também foram realizadas análises de sensibilidade na estratégia de desfoque proposta, de forma a compreender como incertezas na configuração do coletor poderiam impactar a capacidade do controle de implementar um certo valor de desfoque. Esta análise conclui que para que o controle utilizando desfoque em coletores fresnel possa ocorrer sem introduzir incertezas consideráveis no modelo, a posição angular dos espelhos deve apresentar valores de incerteza muito baixos; O quarto e último capítulo de contribuições apresenta uma nova proposta de cálculo do desfoque, agora utilizando inteligência artificial, e mais especificamente redes Neuro-Fuzzy, para realizar o cálculo do desfoque com maior velocidade e precisão. Após treinamento com dados obtidos de um software de referencia para modelagem ótica de coletores solares, a nova proposta se mostrou superior quando comparada com a abordagem baseada em otimização tanto na qualidade dos resultados quanto na velocidade de cálculo; No último capítulo desta tese, é apresentado um resumo dos resultados obtidos, bem como são apresentadas oportunidades para pesquisas futuras a partir dos trabalhos realizados.Abstract: One of the renewable energy sources with the greatest potential is solar irradiation, which provides a significant amount of energy. Due to the low density and high intermittency associated with this source, its exploitation involves the use of concentrators, which have conversion rates that can be considered low when compared to other energy sources. This low efficiency is reflected in the generation costs, and it has been observed that in all solar energy harvesting techniques, these are much higher than the costs possible with traditional solutions. In this context, the development of technologies to capture, store and use solar energy efficiently is crucial for the economic viability of these processes. Unlike more traditional energy sources, the source of solar energy cannot be manipulated directly, so the study of advanced control techniques such as Model Predictive Control (MPC) is interesting to increase the viability of this type of process. This work aims to study predictive control techniques in thermosolar systems with fresnel collectors, in particular hybrid and nonlinear MPC. These controllers are used in problems involving the entire power generation process and the interactions between its components, as well as considering the blur of the collectors used to capture solar energy. The controller proposals presented are developed aiming at a computational cost that allows implementation in real processes, besides promoting some improvement in process productivity indicators. A simplified optical modeling of a Fresnel collector is presented, allowing the understanding of how the optical characteristics of this type of collector affect the process. In addition, a study of possibilities for sun tracking on this type of collector is also shown, evaluating the possible gains that individual manipulation of each collector mirror can bring to the process. Predictive control proposals that consider how the proposed controller blur is implemented are also evaluated. Potential errors or uncertainties arising from the use of blur as a manipulated variable are investigated and, finally, an alternative way to implement the strategy is proposed, using neuro-fuzzy networks. The results show that the proposed predictive controllers are able to control the process avoiding overheating, but for this operation to occur without introducing considerable uncertainties in the controller, the angular position of the mirrors must present very low uncertainty values. The proposed alternative implementation with neuro-fuzzy proved to be superior when compared to the optimization-based approach

Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategyComment: Submitted to Energies - MDPI.co

Fuzzy model predictive control. Complexity reduction by functional principal component analysis

En el Control Predictivo basado en Modelo, el controlador ejecuta una optimización en tiempo real para obtener la mejor solución para la acción de control. Un problema de optimización se resuelve para identificar la mejor acción de control que minimiza una función de coste relacionada con las predicciones de proceso. Debido a la carga computacional de los algoritmos, el control predictivo sujeto a restricciones, no es adecuado para funcionar en cualquier plataforma de hardware. Las técnicas de control predictivo son bien conocidos en la industria de proceso durante décadas. Es cada vez más atractiva la aplicación de técnicas de control avanzadas basadas en modelos a otros muchos campos tales como la automatización de edificios, los teléfonos inteligentes, redes de sensores inalámbricos, etc., donde las plataformas de hardware nunca se han conocido por tener una elevada potencia de cálculo. El objetivo principal de esta tesis es establecer una metodología para reducir la complejidad de cálculo al aplicar control predictivo basado en modelos no lineales sujetos a restricciones, utilizando como plataforma, sistemas de hardware de baja potencia de cálculo, permitiendo una implementación basado en estándares de la industria. La metodología se basa en la aplicación del análisis de componentes principales funcionales, proporcionando un enfoque matemáticamente elegante para reducir la complejidad de los sistemas basados en reglas, como los sistemas borrosos y los sistemas lineales a trozos. Lo que permite reducir la carga computacional en el control predictivo basado en modelos, sujetos o no a restricciones. La idea de utilizar sistemas de inferencia borrosos, además de permitir el modelado de sistemas no lineales o complejos, dota de una estructura formal que permite la implementación de la técnica de reducción de la complejidad mencionada anteriormente. En esta tesis, además de las contribuciones teóricas, se describe el trabajo realizado con plantas reales en los que se han llevado a cabo tareas de modelado y control borroso. Uno de los objetivos a cubrir en el período de la investigación y el desarrollo de la tesis ha sido la experimentación con sistemas borrosos, su simplificación y aplicación a sistemas industriales. La tesis proporciona un marco de conocimiento práctico, basado en la experiencia.In Model-based Predictive Control, the controller runs a real-time optimisation to obtain the best solution for the control action. An optimisation problem is solved to identify the best control action that minimises a cost function related to the process predictions. Due to the computational load of the algorithms, predictive control subject to restric- tions is not suitable to run on any hardware platform. Predictive control techniques have been well known in the process industry for decades. The application of advanced control techniques based on models is becoming increasingly attractive in other fields such as building automation, smart phones, wireless sensor networks, etc., as the hardware platforms have never been known to have high computing power. The main purpose of this thesis is to establish a methodology to reduce the computational complexity of applying nonlinear model based predictive control systems subject to constraints, using as a platform hardware systems with low computational power, allowing a realistic implementation based on industry standards. The methodology is based on applying the functional principal component analysis, providing a mathematically elegant approach to reduce the complexity of rule-based systems, like fuzzy and piece wise affine systems, allowing the reduction of the computational load on modelbased predictive control systems, subject or not subject to constraints. The idea of using fuzzy inference systems, in addition to allowing nonlinear or complex systems modelling, endows a formal structure which enables implementation of the aforementioned complexity reduction technique. This thesis, in addition to theoretical contributions, describes the work done with real plants on which tasks of modeling and fuzzy control have been carried out. One of the objectives to be covered for the period of research and development of the thesis has been training with fuzzy systems and their simplification and application to industrial systems. The thesis provides a practical knowledge framework, based on experience

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

A new T-S fuzzy model predictive control for nonlinear processes

Abstract: In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems