Adaptive High-Order Sliding-Mode Low-speed Control With RBF Neural Network Nonlinear Disturbance Observer for PMSM Drive System

The impact of friction torque, cogging torque, and uncertain disturbances on permanent magnet synchronous motor drive system (PMSMDS) is more pronounced at low speeds compared to high speeds. Therefore, this paper proposes an adaptive integral high-order sliding mode low-speed composite controller (AIHOSMC) based on an RBF neural network nonlinear disturbance observer (RBFNNDO), aimed at enhancing speed tracking accuracy and anti-disturbance capability. Firstly, a PMSMDS model that includes adverse disturbances is established, and the fast-changing and slow-changing characteristics of these disturbances are analyzed. Then, AIHOSMC is developed to enhance dynamic response speeds, eliminate steady-state errors, and dynamically adjust control gains to achieve finite time convergence (FTC) of PMSMDS. Additionally, by combining the nonlinear disturbance observer (NDO) with the infinite approximation capability of RBF neural networks, a RBFNNDO is utilized to accurately estimate fast-changing and slow-changing disturbances in real time, improving the control performance of the AIHOSMC. Thereafter, a closed-loop stability analysis of the proposed controller is performed using Lyapunov theorem. Finally, experimental results validate the effectiveness of the proposed controller, demonstrating significant improvements in low-speed tracking accuracy and anti-disturbance per-formance in PMSMDS.