In this paper we propose a Hybrid PSO–GA optimization method for automatic design of fuzzy logic controllers (FLC) to minimize the steady state error of a plant’s response. We test the optimal FLC obtained by the Hybrid PSO–GA method using benchmark control plants and an autonomous mobile robot for trajectory tracking control. The bio-inspired method is used to find the parameters of the member...

In today‟s world, fuzzy logic and particle swarm optimization are used to answer various engineering problems. In this paper, the proportional integral derivative (PID) controller tuning by fuzzy rule method (FRMs) and a particle swarm optimization (PSO) algorithm are proposed to improve controller by adjusting transfer function parameters. The proposed fuzzy rule function and PSO algorithm sea...

This paper present a new computationally efficient improved stochastic algorithm for solving multi-area DC Optimal Power Flow (DC-OPF) in interconnected power systems. This algorithm is based on the combined application of Fuzzy Logic strategy incorporated in Particle Swarm Optimization (PSO) algorithm, hence termed as Fuzzy PSO (FPSO). Multi-area DC-OPF calculations determine optimum generatio...

This paper discusses a method for improving accuracy of fuzzy-rule-based classifiers using particle swarm optimization (PSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_PSO fuzzy classifier). The second classifier is based on TakagiSugeno fuzzy inference system (TS_PSO fuzzy classifier). The parameters of the ...

In this paper, a 2 DOF planar robot was controlled by Mamdani type-PD-Fuzzy Logic Controller (FLC) tuned with particle swarm algorithm (PSO). In a tuning process of PD-FLC, several trajectories were applied to the robot system. The parameters of fuzzy controller were updated based on squared mean error cost function. The PD-FLC tuned by PSO was tested with a different trajectory. The error of t...

Behaviour-based mobile robots should have an ideal controller to generate perfect behaviour action. A schema to overcome these problems is provided, known as Fuzzy Behaviour-based robot. However, tuning fuzzy parameters is not a simple effort. This paper presents a technique to tune automatically fuzzy Rule Bases and fuzzy Membership Functions (MF) by Particle Swarm Optimisation (PSO), named as...

Particle swarm optimization is a population based optimization technique that is based on probability rules. In this technique each particle moves toward their best individual and group experience had occurred. Fundamental problems of standard PSO algorithm are the falling into the trap of local optimum and its low speed of convergence. One approach for solving the above problems is to combine ...

In this paper, a fuzzy controllers type Takagi_Sugeno is optimized by method of Particle Swarm Optimization (PSO). This algorithm automatically adjust the membership function of fuzzy controllers to control a trajectory of nonholonomic mobile robot that involves path trajectory using two optimized fuzzy controllers one for speed control and the other for azimuth control. The mobile robot is mod...

Particle swarm optimization (PSO) is a well-known instance of swarm intelligence algorithms and there have been many researches on PSO. In this paper, the author proposes an extension of PSO for solving fuzzy-valued optimization problems. In the proposed extension, genotype values (i.e. values in particle position vectors) are not real numbers but fuzzy numbers. Search processes in PSO are exte...