Nonlinear system identification using clustering algorithm and particle swarm optimization

Abstract

The identification of nonlinear systems operating in a stochastic environment is an important problem in various discipline science and engineering. Fuzzy modeling and especially the T-S fuzzy model draw the attention of several researchers in recent decades this is due to their potential to approximate highly nonlinear behavior. An algorithm allowing the identification of the premise and consequent parameters intervening in the T-S fuzzy model at the same time and this starting from the minimization of four optimization criteria is used. A modification on both last optimization criterion is considered. Then an optimization method using the Particle Swarm Optimization method (PSO) is presented in this paper. Particle Swarm Optimization algorithm combined with the proposed algorithm is also presented. Simulation results on a nonlinear system and on a level control system shows that the proposed algorithm combined with the PSO algorithm gives results more effective than the proposed algorithm only more particularly to the level convergence and time computing.

Internal model control - proportional integral derivative controller tuning for first order plus time delayed unstable systems using bacterial foraging algorithm

“…Majid et al (2009), Khalid et Rajinikanth and Latha 3407 al. (2012), Troudi et al (2012) and Rashag et al (2012) proposed PSO based controller tuning for a class systems. Korani et al (2008) and Anguluri et al (2011) discussed about the hybrid algorithm based controller tuning for stable process model.…”