Active Disturbance Rejection Control of Diesel Engine Speed Based on Parameter Self-Learning

The speed control effect has an important influence on the running quality of diesel engines, but the speed is susceptible to fluctuations due to the interference of sudden load and random load. In this paper, an active disturbance rejection control algorithm based on parameter self-learning and active observation of load torque was proposed. Firstly, a dynamic model of crankshaft speed for control was constructed, which is composed of indicated torque, friction torque, and load torque. Secondly, a reduced-order extended state observer was designed based on the dynamic model, which can quickly compensate the load and improve the anti-disturbance ability of speed. Finally, a self-learning algorithm for model parameters was designed to improve model accuracy, so to enhance control quality. The hardware-in-the-loop(HIL) simulation test results show that, compared with the proportional integral differential(PID) algorithm tuned by genetic algorithm, the active disturbance rejection control algorithm has a speed drop of 28 r/min, an improvement of 60.0%, and a speed recovery time of about 1.6 s, a reduction of 23.8%. After adding the model parameter self-learning algorithm, the quality of speed control has been improved by 24.3%. The bench test results show that, in the load step test, compared with the PID algorithm tuned by genetic algorithm, the speed drop of the algorithm proposed is reduced to 18 r/min, a relative improvement of 68.9%. The speed recovery time is reduced to 1.4 s, a relative optimization of 60.0%. © 2022, Editorial Office of the Transaction of CSICE. All right reserved.