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2013•Advanced materials researchRequires access

Study on PID Parameters Tuning Based on Particle Swarm Optimization

Yu Zhou, Jian bin Nie, Ning Han, Chen Chen, Zi Feng Yue

Open publisher page 8 citations

Abstract

To address the PID parameter tuning problem, inspired by the swarm intelligence optimization algorithm, a tuning method of PID parameters based on particle swarm algorithm is proposed. To find an optimal set of PID control parameters in the target space, three parameters of the PID are as particles based on the fitness function. By designing optimization programs, air-conditioning temperature control system experiments were carried out as an example. The results show that the optimization can improve the dynamic control performance of the system.

About this research paper

What this paper is about

To address the PID parameter tuning problem, inspired by the swarm intelligence optimization algorithm, a tuning method of PID parameters based on particle swarm algorithm is proposed. To find an optimal set of PID control parameters in the target space, three parameters of the PID are as particles based on the fitness function. By designing optimization programs, air-conditioning temperature control system experiments were carried out as an example. The results show that the optimization can improve the dynamic control performance of the system.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

To address the PID parameter tuning problem, inspired by the swarm intelligence optimization algorithm, a tuning method of PID parameters based on particle swarm algorithm is proposed. To find an optimal set of PID control parameters in the target space, three parameters of the PID are as particles based on the fitness function. By designing optimization programs, air-conditioning temperature control system experiments were carried out as an example. The results show that the optimization can improve the dynamic control performance of the system.

Key concepts: PID controller, Particle swarm optimization, Control theory (sociology), Multi-swarm optimization, Fitness function, Set (abstract data type), Metaheuristic, Swarm intelligence

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