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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)
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 paper we describe the application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of PI controller and to find the best optimal intelligent controller. The ACO algorithm is a bio-inspired optimization method that has proven its success through various combinatorial optimization problems. The parameters of the(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)
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