2005•Kongzhi yu jueceRequires access

A nonlinear PID controller based on genetic tuning algorithm

Hua Han

Open publisher page 16 citations

Abstract

The relationship between the error signal and gain parameters of PID controller is nonlinear , fitting and constructing the nonlinear function of each parameter can be used respectively for the individual tuning each part of PID controller. In this article, it is described that based on the tuning law between the control and error, and (formulating) the nonlinear function of each gain parameter, the nonlinear PID controller could be optimized and (constructed) by adopting genetic algorithm. The simulate results of typical system shows that the dynamic and static (performances) of the system can be both attended to at a certain degree.

About this research paper

What this paper is about

The relationship between the error signal and gain parameters of PID controller is nonlinear , fitting and constructing the nonlinear function of each parameter can be used respectively for the individual tuning each part of PID controller. In this article, it is described that based on the tuning law between the control and error, and (formulating) the nonlinear function of each gain parameter, the nonlinear PID controller could be optimized and (constructed) by adopting genetic algorithm. The simulate results of typical system shows that the dynamic and static (performances) of the system can be both attended to at a certain degree.

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OpenAlex reports 16 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

The relationship between the error signal and gain parameters of PID controller is nonlinear , fitting and constructing the nonlinear function of each parameter can be used respectively for the individual tuning each part of PID controller. In this article, it is described that based on the tuning law between the control and error, and (formulating) the nonlinear function of each gain parameter, the nonlinear PID controller could be optimized and (constructed) by adopting genetic algorithm. The simulate results of typical system shows that the dynamic and static (performances) of the system can be both attended to at a certain degree.

Key concepts: PID controller, Control theory (sociology), Nonlinear system, Genetic algorithm, Controller (irrigation), Computer science, Function (biology), Control engineering

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