Autotuned Nonlinear Extended State Observer-Based Fixed-Time Control for Motor-Drive Servo Systems
Ruiqi Xu, Jianxing Liu, Xinpo Lin, Zhuang Liu, Fei Yan, Yabin Gao
Abstract
To enhance the position tracking performance of permanent magnet synchronous motors (PMSMs) under unknown disturbances, this paper proposes a fast fixed-time control strategy based on an improved nonlinear extended state observer (NESO). First, load disturbances and nonlinear friction are considered as lumped disturbance, for which a NESO is designed to estimate the lumped disturbance. The control parameters of the NESO are tuned online using a neural network optimization algorithm, eliminating the need for offline training. Then, a robust fixed-time sliding mode control method is proposed, based on an improved nonsingular fast terminal sliding mode manifold, which offers better convergence performance. The Lyapunov method is used to prove the fixed-time stability of the position tracking error system. Finally, the effectiveness of the proposed method is validated on an experimental platform with PMSMs, and it is compared with other advanced fixed-time position control methods. The comparison results confirm that the proposed method exhibits superior control performance.