Parameter tuning of active disturbance rejection control based on improved differential evolution algorithm

Abstract

Aiming at the problem that it is difficult to obtain the optimal parameters and performance of nonlinear active disturbances rejection controller (ADRC) by the method of conventional empirical turning, a parameter tuning method based on improved differential evolution algorithm (DE) is proposed to enhance the accuracy of the controller. Firstly, to balance the global and local search abilities appropriately, the random neighborhood-based mutation strategy is proposed. In addition, a history-driven parameters self-adaptation method is implemented to enhance the accuracy of the optimization and accelerate the searching progress. Lastly, the generalized opposition-based learning (GOBL) scheme is applied to avert the DE getting trapped in local optimum and improve the diversity of the population. The result of optimized ADRC shows that it has less overshoot and higher control accuracy. After adding external disturbance, the optimized ADRC can still maintain perfect performance of control which indicates that it has good anti-interference ability.