PID control optimization method for vibration system based on grey random differential evolution algorithm

Abstract

The invention provides a PID control optimization method for a vibration system based on a grey random differential evolution algorithm. The method comprises the steps of carrying out initialization on a PID control parameter, i.e. a target vector, calculating and updating a mutation factor and the crossover probability according to the grey correlation and a random adaptive strategy, then carrying out a mutation operation, a crossover operation and a selection operation to obtain the target vector of the next generation, i.e. the new PID control parameter, and iterating in the above mode until the set maximum number of iterations is reached. In the invention, the grey random differential evolution algorithm can update the mutation factor and the crossover probability by using a grey random adaptive mechanism, so that the optimal PID control parameter of the vibration system can be efficiently searched with a smaller number of iterations and a smaller population size.

Description

technical field [0001] The invention belongs to the technical field of control algorithms, and in particular relates to a PID control optimization method of a vibration system based on a gray stochastic differential evolution algorithm. Background technique [0002] PID control technology is one of the most widely used control methods for vibration systems at present, and the tuning of PID control parameters is the core problem that PID technology needs to solve. The traditional empirical value method, Z-N method, etc. are not ideal for the control of complex nonlinear vibration systems, while the evolutionary algorithm (EA) based on the idea of ​​biological evolution is a mature global vibration control system with high robustness and wide applicability. The optimization method can continuously search for the optimal PID control parameters in an iterative way according to the system performance index, so as to achieve the effect of optimal control. The EA-based PID optimiz...

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Problems solved by technology

Although the PID optimization control method based on the DE algorithm has a satisfactory optimization effect, there are still two problems in the following aspects: 1) The selection of the main parameters of the DE algorithm, such as the variation factor and crossover probability, mainly adopts the empirical value method, so that the DE algorithm Lack of self-adaptability, such as improper selection of DE algorithm parameters may lead to unsatisfactory vibration control effect; 2) DE algorithm requires more iterations to obtain excellent optimal PID parameters, resulting in large calculation time and memory consumption, which is not conducive to On-line Optimal Adjustment of PID Parameters

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