PID Parameters Tuning Based on Self-Adaptive Hybrid Particle Swarm Optimization Algorithm
A new particle swarm optimization algorithm based on the self-adaptive adjustment to inertia weight is proposed to overcome problems of slow convergence velocity for ending and easily plunging into the local optimum. This algorithm can dynamically adjust inertia weight by introducing conception of particle distance which can reflect aggregation level of particles. Meanwhile, we used chaotic map to initialize particle swarm, which can solve blindness at the searching initiate and reduce probability of plunging into local optimum. To test the availability of advanced algorithm, we applied it to PID parameters’ tuning of control systems. The simulation results in this paper reveal that system control performance is superior with normal particle swarm optimization and genetic algorithm which proves its availability and reliability.
KeywordsSelf-adaptive convergence velocity Chaos Particle swarm optimization
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