Showing papers by "Mahdi Aliyari Shoorehdeli published in 2008"

Load frequency control in interconnected power system using multi-objective PID controller

Abstract: In this paper designing of multi-objective PID controller for load frequency control (LFC) based on adaptive weighted particle swarm optimization (AWPSO) has been proposed. Conventional methods such as Ziegler-Nichols and Cohen-Coon are based on trial-and-error and their best performances are achieved for first-order process. Single-objective population based methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have only one solution in a single run. Unlike single objective methods, multi-objective optimization can find different solutions in a single run. In the proposed method, overshoot/undershoot and settling time are used as objective functions for multi-objective optimization. The proposed method is used for designing of PID parameters for two area interconnected power system.

Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters as an Identifier Using Fuzzy PSO

Abstract: This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS) and a new type of particle swarm optimizers (PSO). The previous works emphasized on gradient base method or least square (LS) based method. This study applied one of the swarm intelligent branches, PSO. The hybrid method composes fuzzy PSO with recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from fuzzy systems method and using fuzzy rules for tuning PSO parameters during training algorithms. The simulation results show that in comparison with current gradient based training, and authors previous hybrid method the proposed training have a good adaptation to complex plants and train less parameter than gradient base methods.

Integer GA for mobile robot path planning with using another GA as repairing function

Abstract: In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm which was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Another genetic algorithm is used to repair some paths which collide with obstacles. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.

Sliding Mode Control of Magnetic Levitation System Using Radial Basis Function Neural Networks

Abstract: This paper has developed a sliding mode controller (SMC) based on a radial basis function model for control of magnetic levitation system. Adaptive neural networks controllers need plant's Jacobain, but here this problem solved by sliding surface and generalized learning rule in case to eliminate Jacobain problem. The simulation results show that this method is feasible and more effective for magnetic levitation system control.

Path planning of mobile robot using integer GA with considering terrain conditions

Abstract: In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm that was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.

Sliding Mode Control of Flexible Joint Using Gaussian Radial Basis Function Neural Networks

Abstract: This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller,utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.

Identification of Fuzzy Models Using Cartesian Genetic Programming

Abstract: Fuzzy models have capability for solving problem in different application such as pattern recognition, prediction and control. Nevertheless, it has to be emphasized that the identification of a fuzzy model is complex task with many local minima. Cartesian genetic programming provides a way to solve such complex optimization problem. In this paper, fuzzy model is in form of network. Cartesian genetic programming is used to optimize the antecedent part and recursive least square is used to optimized the consequent part. The initialization of membership function parameters are doing with fuzzy clustering. Benefit of the methodology is illustrated by simulation results.