Tuning of Auto Disturbance Rejection Controller Parameters Based on Improved Grey Wolf Optimizer

Abstract

Aiming at the problem that auto disturbance rejection controller (ADRC) requires too many tuning parameters, this paper proposed an improved grey wolf optimizer algorithm to tune the parameters of ADRC, and used the ADRC with tuned parameters to control the electro-hydraulic position servo system. Based on the original grey wolf optimizer algorithm (GWO), the linear convergence factor was improved to a non-linear mode to optimize the optimization path, and according to the parameter adjustment advantages of particle swarm optimization, the learning factors were introduced in the process of updating the position to give the wolves consciousness to avoid local optima and improve the convergence speed. Through the test functions, simulation and experimental tests, it was found that the improved grey wolf optimizer had higher convergence accuracy, and the ADRC under the improved grey wolf optimizer parameters tuning could achieve the anti-interference control effect well.