2007Unpublished venueRequires access

Particle Swarm Optimization for PID Controllers with Robust Testing

Shih-Feng Chen

Open publisher page 18 citations

Abstract

In this paper a design method which optimizes PID parameters of motion systems via the particle swarm algorithm (PSO) is presented. Simulations and experiments show that the PID controller via the particle swarm optimization (PSO-PID) performs better time response than the known genetic algorithm method. Through the results, the PSO-PID controller has also been proven to be more efficient than the genetic algorithm in seeking for the global optimum PID parameters with respect to the desired performance indices. In addition, the robust testing for the PID controller via PSO has been presented well.

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What this paper is about

In this paper a design method which optimizes PID parameters of motion systems via the particle swarm algorithm (PSO) is presented. Simulations and experiments show that the PID controller via the particle swarm optimization (PSO-PID) performs better time response than the known genetic algorithm method. Through the results, the PSO-PID controller has also been proven to be more efficient than the genetic algorithm in seeking for the global optimum PID parameters with respect to the desired performance indices. In addition, the robust testing for the PID controller via PSO has been presented well.

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OpenAlex reports 18 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper a design method which optimizes PID parameters of motion systems via the particle swarm algorithm (PSO) is presented. Simulations and experiments show that the PID controller via the particle swarm optimization (PSO-PID) performs better time response than the known genetic algorithm method. Through the results, the PSO-PID controller has also been proven to be more efficient than the genetic algorithm in seeking for the global optimum PID parameters with respect to the desired performance indices. In addition, the robust testing for the PID controller via PSO has been presented well.

Key concepts: PID controller, Particle swarm optimization, Control theory (sociology), Genetic algorithm, Computer science, Multi-swarm optimization, Controller (irrigation), Control engineering

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