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In this paper, an adaptive fuzzy controller (AFC) for a certain class of unknown nonlinear systems is proposed. The proposed approach employs a fuzzy system to approximate the unknown functions in designing the adaptive controller and an observer is designed to generate an error signal for the adaptive law. The free parameters of the AFC can be tuned on(More)
In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST(More)
In this paper we develop a model predictive control (MPC) approach for regulating DC-DC Boost converters. The proposed control strategy is implemented and tested using two models: an averaged non-linear model for control purposes and a switched Buck-Boost circuit model as the controlled plant. The main objective of the paper is to design a Fuzzy Predictive(More)
This paper introduces a new approach for designing an adaptive fuzzy model predictive control (AFMPC) using the Particle Swarm Optimization (PSO) algorithm. The system to be controlled is modeled by a Takagi-Sugeno fuzzy inference system whose parameters are identified using recursive least square algorithm. These parameters are used to calculate the(More)
In this work, a model based T-S fuzzy predictive control LMI optimization is introduced. The aim of discrete T-S fuzzy predictive controller is to drive the state of the system to the original state where a stabilizing controller is ensured. The stability of the controlled systems is studied using non quadratic case of the Lyapunov function and adopting of(More)
Using a linear model predictive controller (MPC) as a controller of nonlinear system may not give a satisfactory dynamic performance. This has led to the development of a number of nonlinear MPC (NMPC) approaches that permit the use of first principles based nonlinear models. This paper presents an algorithm to solve the problem of non-linear predictive(More)
In this paper, a photovoltaic pumping system is controlled using a Model Predictive Control (MPC) approach. The non-linearity of controlled system, which is a photovoltaic (PV) water pumping system, was modelled using a Takagi-Sugeno(T-S) fuzzy model. The Buck converter of the controlled system is coupling with Photovoltaic (PV) generator to a DC motor pump(More)