Self-tuning method for a linear active disturbance rejection controller

This paper introduces a parameter self-tuning algorithm based on dynamic response time series data mining to solve the parameter self-tuning difficulty of a linear active disturbance rejection controller(LADRC). In the algorithm,two gain signal loops' dynamic response of linear state error feedback(LSEF) is used as the parameter adjustment object. Through the parameters auto-tuned by NLJ algorithm and the process data recorded,the control parameter adjustment policy based on association mining is applied to LADRC parameter auto-tuning. To verify the actual effect of the parameter self-tuning method in this paper,a hydraulic automatic position control(HAPC) system is used as the control object. Step response simulation and Monte Carlo experiment show that the dynamic response of the system which is combined by HAPC and the controller is better,the robustness is stronger,the adjustment time is shorter than NLJ algorithm,and the overshoot is also less than the traditional LADRC controller. It is considered as a practical LADRC controller parameter self-tuning method.