Fixed-Time Integral Sliding Mode Control Integrating RBF Network and Super-Twisting ESO for SPMSM

Abstract This paper addresses the speed regulation performance degradation in surface-mounted permanent magnet synchronous motor (SPMSM) drive systems caused by model uncertainties, parameter perturbations, and external load disturbances. To enhance both dynamic response and robustness, this paper propose a composite control strategy integrating a radial basis function (RBF) neural network, a fixed-time integral terminal sliding mode controller (FITSMC), and a super-twisting extended state observer (STESO). The FITSMC guarantees fixed-time error convergence with a bounded settling time independent of initial states, while the integral action eliminates steady-state errors. The RBF network online approximates internal parameter variations and unmodeled dynamics, whereas the STESO estimates external load disturbances for feedforward compensation. This coordinated disturbance rejection mechanism reduces the required switching gain, effectively suppressing sliding mode chattering without sacrificing robustness. Rigorous Lyapunov analysis establishes the fixed-time stability of the closed-loop system. Comparative simulations under no-load startup, sudden load change, and full-load operation demonstrate that the proposed RBF-FITSMC-STESO achieves faster convergence, smaller speed drops, and lower steady-state fluctuations than conventional PI control, standard sliding mode control, and FITSMC-STESO without RBF adaptation, confirming its effectiveness for high-performance SPMSM speed regulation.