This paper presents a sliding mode observer (SMO) with new reaching law (NRL) for observing the real-time linear speed of a controllable excitation linear synchronous motor (CELSM). For the purpose of balancing the dilemma between the rapidity requirement of dynamic performance and the chattering reduction on sliding mode surface, the proposed SMO with NRL optimizes the reaching way of the conventional constant rate reaching law (CRRL) to the sliding mode surface by connecting the reaching process with system states and the sliding mode surface. The NRL is based on sigmoid function and power function, with proper options of exponential term and power term, the NRL is capable of eliminating the effect of chattering on accuracy of the angular position estimation and speed estimation. Compared with conventional CRRL, the SMO with NRL achieves suppressing the chattering phenomenon and tracking the transient process rapidly and accurately. The stability analysis is given to prove the convergence of the SMO through the Lyapunov stability theory. Simulation and experimental results show the effectiveness of the proposed NRL method.

This paper proposes an improved sliding mode observer (ISMO) for the current decoupling control of the linear synchronous motor (LSM) to improve the dynamic performance of the LSM system. The proposed ISMO is based on variable power and exponential terms. By comparing the convergence time of the proposed ISMO and the conventional sliding mode observer (CSMO), we find that the ISMO can achieve a high convergence rate than the CSMO with the appropriate parameter options. Because the ISMO is established based on the extended state equation of the lumped disturbances, which includes the current coupling interferences and parameter variation disturbances, the ISMO-based decoupling control (ISMO-DC) can suppress these two types of disturbances simultaneously. The stability analysis based on the Lyapunov stability theory guarantees the convergence of the proposed ISMO. The experimental results verify the effectiveness of the proposed method in achieving a better decoupling control performance for the LSM system.

This paper proposes a novel nonlinear autoregressive with exogenous input-based adaptive recursive least squares model-free predictive control (NARX-ARLS-MFPC) strategy for permanent magnet synchronous motor (PMSM) drives. The core challenge addressed is the performance degradation of conventional model predictive control (MPC) under inevitable motor parameter mismatches. The proposed method integrates the NARX model with an ARLS algorithm featuring a variable forgetting factor to dynamically track system changes. Comprehensive simulation studies validate the superior robustness of the strategy. Under significant inductance and flux linkage mismatches, the proposed method reduces current total harmonic distortion (THD) by 28.3% and 12.3% compared to conventional finite-control-set model predictive control (FCS-MPC) and a baseline control method, respectively. It maintains stable performance under moderate sensor noise with appropriate tuning. During load transients combined with resistance and inductance mismatches, it achieves THD reductions of 24.1% and 15.7% versus the two benchmark methods, respectively. Statistical analysis under parameter perturbations confirms its overall superior performance across key dynamic and steady-state metrics. The results demonstrate that the synergistic integration of the nonlinear model and adaptive identification effectively suppresses current harmonics caused by model inaccuracies while enhancing dynamic performance.