A PID controller parameter optimal setting method based on a differential evolution method

Abstract

The invention discloses a PID controller parameter optimal setting method relating to the field of automatic control and based on swarm intelligence optimization searching technology. Integral performance indexes which can comprehensively measure stability, rapidness and accuracy of an automatic control system are adopted as fitness functions. Through utilization of a global optimization function of a differential evolution algorithm, a proportionality coefficient K[p], an integral coefficient K[I] and a differential coefficient K[D] which can realize global minimization of the performance index function values of a PID control system are searched to be regarded as optimal setting parameters of the PID controller. The PID controller parameter optimal setting method based on the differential evolution method is utilized to carry out simulation experiment direct current motor rotating speed closed loop control system. The experiment result shows that a PID control system obtained after undergoing setting by the method has outstanding advantages of a fast adjusting speed and small overshoot compared with control systems set obtained through a common setting method. The PID controller parameter optimal setting method is a PID controller parameter setting method having a popularization value.

Description

technical field [0001] The invention belongs to the technical field of automatic control, and relates to a PID controller parameter setting technology based on a swarm intelligence optimization algorithm. Background technique [0002] PID control is one of the earliest developed control strategies. It is widely used in industrial process control due to its simple algorithm, good robustness and high reliability. However, in the actual use process, due to the trouble of complicated parameter setting methods, the parameters of the PID controller are often poorly set and the performance is not good, and it is difficult to achieve the expected effect. [0003] Differential Evolution (DE) algorithm is a random heuristic search algorithm formed by simulating the evolutionary development law of natural biological populations based on the principle of "survival of the fittest and survival of the fittest". It is an emerging evolutionary computing technology. [0004] Therefore, it is...

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Problems solved by technology

However, in actual use, due to the troubles of complicated parameter tuning methods, the parameters of the PID controller are often poorly tuned and the performance is not good, and it is difficult to achieve the expected effect.

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