2010Gong-kuang zidonghuaRequires access

Optimization of PID Controller Parameters Based on Modified Particle Swarm Optimization Algorithm

Wei Xiao-bin

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Abstract

An optimization method based on modified particle swarm optimization(PSO) algorithm was proposed for tuning PID controller parameters.On the basis of real coding and setting search space of parameters,the method uses idea of decreasing inertia weight of an exponential curve to greatly improve algorithm's convergence speed and accuracy,and embeds local search strategy based on mutation operator of differential evolution algorithm to raise flexibility of individual particle and diversity of population,improve solution quality,and enhance the balance between the algorithm's global space exploration and local area improved capacity.The simulation results showed that the controller parameters from the new solution can make control system have better dynamic response characteristics and satisfactory control effect than traditional and intelligent algorithm.

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An optimization method based on modified particle swarm optimization(PSO) algorithm was proposed for tuning PID controller parameters.On the basis of real coding and setting search space of parameters,the method uses idea of decreasing inertia weight of an exponential curve to greatly improve algorithm's convergence speed and accuracy,and embeds local search strategy based on mutation operator of differential evolution algorithm to raise flexibility of individual particle and diversity of population,improve solution quality,and enhance the balance between the algorithm's global space exploration and local area improved capacity.The simulation results showed that the controller parameters from the new solution can make control system have better dynamic response characteristics and satisfactory control effect than traditional and intelligent algorithm.

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Available abstract

An optimization method based on modified particle swarm optimization(PSO) algorithm was proposed for tuning PID controller parameters.On the basis of real coding and setting search space of parameters,the method uses idea of decreasing inertia weight of an exponential curve to greatly improve algorithm's convergence speed and accuracy,and embeds local search strategy based on mutation operator of differential evolution algorithm to raise flexibility of individual particle and diversity of population,improve solution quality,and enhance the balance between the algorithm's global space exploration and local area improved capacity.The simulation results showed that the controller parameters from the new solution can make control system have better dynamic response characteristics and satisfactory control effect than traditional and intelligent algorithm.

Key concepts: Particle swarm optimization, PID controller, Control theory (sociology), Inertia, Mathematical optimization, Convergence (economics), Differential evolution, Controller (irrigation)

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