LI Z J, WANG N, ZHANG X Y,et al.Parameter tuning of second-order linear active disturbance rejection control based on Bayesian optimization[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(3):85-92.

doi:10.16186/j.cnki.1673-9787.2024110009

Received:2025/02/17

Revised:2025/04/07

Published:2026/04/28

Parameter tuning of second-order linear active disturbance rejection control based on Bayesian optimization

Li Zhuangju1, Wang Ning1, Zhang Xiaoyu1, Wei Wei2

1.School of Electrical and Information Engineering， Beijing University of Civil Engineering and Architecture，Beijing  102616， China；2.School of Automation， Beijing University of Posts and Telecommunications， Beijing  100876， China

Abstract: Objectives Linear Active Disturbance Rejection Control （LADRC） is a control method that has low dependence on the system model and shows promising application prospects in industrial processes. However， its parameter tuning is complicated， leading to high trial and error costs in industrial applications. To address this issue， an engineering oriented parameter tuning method for LADRC is proposed.  Methods First， by analyzing the equivalent structure of the second order LADRC， the equivalent relationship between LADRC parameters and PID parameters is established. Then， initial PID parameters are obtained through Bayesian optimization. Finally， the initial LADRC parameters are derived using the established equivalent relationship. Results Simulation experiments are carried out on several typical transfer functions in Simulink. The results show that for various types of controlled plants， the second order LADRC outperforms PID control in terms of stability and accuracy. Physical experiments are conducted using a PLC based temperature control system. The model of the controlled plant is identified， the initial PID parameters are obtained via Bayesian optimization， and the LADRC controller is implemented in the PLC program. Then， both PID and LADRC control experiments are performed. The results indicate that in the temperature step response， the second order LADRC system achieves a settling time of approximately 80 seconds， an overshoot of about 2.3%， very small steady state error， and a good ITAE index. In contrast， the PID controller yields a settling time of about 95 seconds， an overshoot of about 1.6%， and a moderate ITAE index.  Conclusions The proposed parameter initialization method based on Bayesian optimization ensures the effectiveness of parameter transfer between different platforms， reduces tuning effort， and provides a powerful parameter adjustment strategy for the widespread application of second order LADRC.

Key words:parameter tuning;Bayesian optimization;linear active disturbance rejection control;PID control;PLC