A PSO-based adaptive fuzzy PID-controllers

Chiou, Juing-Shian; Tsai, Shun-Hung; Liu, Mingtang

doi:10.1016/j.simpat.2012.04.001

Cited by 130 publications

(54 citation statements)

References 16 publications

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“…El primer término ω•v id (t); le da a la partícula una inercia, de tal manera que la partícula tienda a seguir el comportamiento que llevaba y así amortiguar la velocidad de la misma [6]. El término φ1•rand1(pBesti-xid(t)); permite que la velocidad de la partícula sea influenciada por las mejores posiciones que la misma ha tenido durante su recorrido por el espacio de búsqueda, a este término se le conoce como componente cognitiva [10], y el término φ2•rand2(gBest-xid(t)); permite que la partícula pueda dirigirse hacia las mejores posiciones que han encontrado otras partículas dentro del enjambre, a este término se le conoce como el componente social [8]. De esta manera, si una partícula encuentra una muy buena posición, las otras tendrán la tendencia a dirigirse hacia ese punto óptimo encontrado.…”

Section: Optimización Por Enjambres De Partículas (Pso)unclassified

“…En el campo del control adaptativo se ha usado variaciones de PSO para encontrar los parámetros de un sistema no lineal y de un controlador PID [9], también se ha usado PSO para estimar los parámetros de una PID difuso [10]. Algo que tienen en común los trabajos mencionados, es que el análisis se realiza haciendo uso de simulaciones en contraste, en este trabajo se muestra como realizar la auto-sintonización de un control PID en con herramientas industriales como son los PLC y el estándar de comunicación OPC (OLE for Process Control).…”

Section: Introductionunclassified

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Sintonización De Un Controlador PID En Un PLC Haciendo Uso De Inteligencia De Enjambres/Auto-tuning of a PID controller implemented in a PLC using swarm intelligence

Sotelo

Marín²,

Navas³

2017

prospect

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RESUMENEste trabajo muestra la implementación en un PLC Allen Bradley de una técnica de inteligencia de enjambres cuya función es determinar el modelo matemático de un sistema o proceso. Con el modelo del sistema encontrado, se calculan los parámetros de un controlador PID que garantizan un comportamiento deseado del sistema. La técnica de inteligencia de enjambres usada para la obtención del modelo del proceso es conocida como optimización por enjambre de partículas. Para probar el funcionamiento del algoritmo implementado en el PLC, se realizaron simulaciones de sistemas dinámicos en un computador de escritorio con el software Matlab, desde este software se estableció conexión con el PLC usando como estándar de comunicación el OPC (OLE for Process Control). De esta manera, Matlab envía información sobre el estado del proceso, y el PLC estima el modelo matemático del sistema, sintoniza el controlador PID para luego enviar al PC una acción de control adecuada. Los resultados mostraron que al comparar el funcionamiento de un PID fijo con el PID auto-sintonizado sus desempeños son similares lo cual es un buen punto de partida para futuras mejoras al esquema presentado. Una de las principales conclusiones de este trabajo es la posibilidad de utilizar algoritmos de control adaptivo usando inteligencia de enjambres en un ambiente industrial.Palabras clave: Automatización; PLC; Inteligencia artificial; PID; Inteligencia de enjambres; AutoSintonía. ABSTRACTThis project implements a swarm intelligence algorithm in an Allen Bradley PLC. The task of the algorithm is to estimate a mathematical model of a system or process to allow finding parameters of a PID controller. The algorithm is named particle swarm intelligence and it emulates the behavior of a flock of birds when they are flying. The test of the algorithm in the PLC was made using mathematical models of systems that were simulated in Matlab running over a personal computer (PC). So, a connection between the PLC and PC was made using OPC (OLE for Process Control). This way, Matlab sends information about the state of the process, and the PLC estimates the mathematical model of the system, tuning the PID controller and then it sends to PC a suited control action. The results show that the performance of the system controlled with the adaptive PID is similar to the system controlled using a standard PID. This project shows the potential of to use adaptive control with swarm intelligence on industrial environments.

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Section: Optimización Por Enjambres De Partículas (Pso)unclassified

Section: Introductionunclassified

Sintonización De Un Controlador PID En Un PLC Haciendo Uso De Inteligencia De Enjambres/Auto-tuning of a PID controller implemented in a PLC using swarm intelligence

Sotelo

Marín²,

Navas³

2017

prospect

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“…Much research work has been devoted to the subject of FLC online optimization [28][29][30]. A proper selection of the FLC rule base and/or the MFs parameters would significantly improve the control action while the system is operating.…”

Section: Pso-fuzzy Logic Controller (Pso-flc)mentioning

confidence: 99%

An Intelligent Fuzzy Logic Controller for Maximum Power Capture of Point Absorbers

et al. 2014

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This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as "Point Absorber". The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics.

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“…This research proposes an equivalent fuzzy PID controller which has a simple PID structure design with a 3-dimensional fuzzy rule table, instead of the combination of different fuzzy PID structural elements or a hybrid controller structure [14]. Moreover, to achieve optimal control performance for a FLC, some artificial intelligent techniques such as Genetic Algorithm and Neural Network are efficient approaches [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…But based on this study, a fuzzy PID controller may outperform a conventional PID controller quickly by fine-tuning the MFs of the fuzzy variables. We have found that some learning-based techniques or evolutionary algorithms have been applied in the optimal FLC design [14][15][16]. Experienced researchers should agree on the importance of setting initial values or weights in the learning system, which will greatly influence the learning results and convergence speed.…”

mentioning

confidence: 99%

Equivalence between Fuzzy PID Controllers and Conventional PID Controllers

et al. 2017

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This paper proposes the equivalence between fuzzy Proportional-Integral-Derivative (PID) controllers and conventional PID controllers. A well-designed conventional PID controller, with the help of the proposed method, can be rapidly transformed to an equivalent fuzzy logic controller (FLC) by observing and defining the operating ranges of the input/output of the controller. Furthermore, the knowledge base of the proposed equivalent fuzzy PID controller is represented as a cube fuzzy associative memory (FAM), instead of a combination of PD-type and PI-type FLCs in most research. Simulation results show the feasibility of the proposed technique, both in continuous and discrete time. Since the design techniques of conventional linear PID controllers have matured, they can act as preliminary expert knowledge for nonlinear FLCs designs. Based on the proposed equivalence relationship, the designer can further tune the membership functions of fuzzy variables in the control rules to exhibit the nonlinearity of a FLC and yield more satisfactory system responses in an efficient way.

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A PSO-based adaptive fuzzy PID-controllers

Cited by 130 publications

References 16 publications

Sintonización De Un Controlador PID En Un PLC Haciendo Uso De Inteligencia De Enjambres/Auto-tuning of a PID controller implemented in a PLC using swarm intelligence

Sintonización De Un Controlador PID En Un PLC Haciendo Uso De Inteligencia De Enjambres/Auto-tuning of a PID controller implemented in a PLC using swarm intelligence

An Intelligent Fuzzy Logic Controller for Maximum Power Capture of Point Absorbers

Equivalence between Fuzzy PID Controllers and Conventional PID Controllers

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