2006•Jisuanji fangzhenRequires access

Genetic Simulated Annealing Algorithm of Controller Parameter Optimization for Time-delay Systems

Yang Xiao-jun

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Abstract

In this paper,a controller parameter optimization method based on the genetic simulated annealing algorithm for time-delay system is proposed.GA toolbox-based MATLAB is simultaneously modified to apply to PID parameter optimization.Genetic simulated annealing algorithm is a new kind of global searching algorithm,which is based on the genetic algorithm and the simulated annealing algorithm.It can overcome a lot of GA's defects,such as premature convergence,bad local searching ability and GA deceptive problems.An optimization of PID parameter of a time-delay system is presented.Simulation results show this new strategy is effective.

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What this paper is about

In this paper,a controller parameter optimization method based on the genetic simulated annealing algorithm for time-delay system is proposed.GA toolbox-based MATLAB is simultaneously modified to apply to PID parameter optimization.Genetic simulated annealing algorithm is a new kind of global searching algorithm,which is based on the genetic algorithm and the simulated annealing algorithm.It can overcome a lot of GA's defects,such as premature convergence,bad local searching ability and GA deceptive problems.An optimization of PID parameter of a time-delay system is presented.Simulation results show this new strategy is effective.

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Available abstract

In this paper,a controller parameter optimization method based on the genetic simulated annealing algorithm for time-delay system is proposed.GA toolbox-based MATLAB is simultaneously modified to apply to PID parameter optimization.Genetic simulated annealing algorithm is a new kind of global searching algorithm,which is based on the genetic algorithm and the simulated annealing algorithm.It can overcome a lot of GA's defects,such as premature convergence,bad local searching ability and GA deceptive problems.An optimization of PID parameter of a time-delay system is presented.Simulation results show this new strategy is effective.

Key concepts: Simulated annealing, PID controller, Adaptive simulated annealing, Genetic algorithm, Meta-optimization, MATLAB, Computer science, Toolbox

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