ESO-Based Direct Model-Free Adaptive Predictive Compensation Control for Permanent Magnet Synchronous Motors

Abstract

For a class of permanent magnet synchronous motors (PMSM) characterized by strong coupling, significant nonlinearity, load uncertainties, and external disturbances, this paper investigates an extended state observer-based direct model-free adaptive predictive compensation control (ESO-dMFAPCC) method for speed regulation. Initially, a dynamic linearization model of a PMSM system with unknown external disturbances is developed. Subsequently, a novel direct model-free adaptive predictive compensation controller, theoretically equivalent to an ideal controller, is proposed. An extended state observer (ESO) is designed to estimate the state variable, capturing the characteristics of the external disturbance and system uncertainty. On this basis, control gains with online adaptive capabilities are designed to achieve adaptive speed control for the PMSM system. The primary advantage of the proposed control method lies in its novel framework for directly constructing a predictive compensation controller, which simplifies the controller structure design process by reducing it to a parameter-optimization problem. Moreover, utilizing only the PMSM system’s input/output (I/O) data, the proposed ESO-dMFAPCC is purely data-driven and exhibits strong robustness against external disturbances. Furthermore, the stability of the closed-loop PMSM system is rigorously analyzed. Finally, the simulation results demonstrate that, compared to PI control, sliding mode control (SMC), and model-free adaptive predictive control (MFAPC), the proposed method offers superior dynamic performance while significantly enhancing the anti-disturbance capabilities of the PMSM system.

Keywords

• Permanent magnet synchronous motor
• model-free adaptive predictive control
• extended state observer
• speed control
• anti-disturbance