Design of Adaptive Steady-State Fading Memory Filter for Servo Drive Systems Under Maneuvering Conditions

Adaptive Mixed-State Feedback Speed Control for Active Disturbance Rejection in PMSM Drives

Integrated-criterion-based PI parameter tuning for a permanent magnet synchronous motor servo system using an improved multi-verse optimizer

To improve the tracking performance and dynamic response of a permanent magnet synchronous motor servo system, this study investigates a performance-oriented parameter-tuning framework for the controllers in the servo loops. Rather than proposing a new control structure, the work focuses on the formulation and solution of the tuning problem under stringent requirements on both transient and steady-state performance. A comprehensive optimization criterion is established by jointly considering overshoot, settling time, and steady-state error from a global performance perspective. Based on this criterion, an improved multi-verse optimizer with nonlinear parameter scheduling and an inertia-weight mechanism is employed to search for controller parameters with a better balance between exploration and exploitation. The proposed tuning framework is evaluated on a MATLAB/Simulink co-simulation platform under tracking and load-disturbance conditions. Simulation results indicate that, compared with the baseline tuning method, the optimized controller parameters can improve dynamic response characteristics and reduce overshoot and steady-state error. Nevertheless, the present validation is limited to co-simulation, and hardware-level experimental verification will be pursued in future work to further assess practical applicability.

Predefined-time sliding mode control for aerial manipulator based on the robust adaptive learning rate neural network state observer

Decoupled Koopman-Based Tracking for Enhanced Convergence in Dynamic State Estimation of Power Systems

Dynamic state estimation (DSE) plays a critical role in the monitoring and control of modern electrical power systems, particularly under time-varying operating conditions. Conventional estimators such as the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) rely on nonlinear dynamic models in the prediction stage, which may introduce computational complexity and numerical sensitivity. This paper proposes a decoupled Koopman-based tracking framework in which a data-driven Koopman operator is employed exclusively as a linear prediction mechanism prior to the estimation stage, while preserving the classical correction structure of conventional estimators. The approach is validated on the IEEE 14-bus system under dynamic load variations over 300 operating points. Performance is evaluated in terms of global and nodal root mean square error (RMSE), measurement residual metrics, execution time statistics, and operational robustness under latency constraints. The results show that the Koopman-enhanced estimators maintain competitive estimation accuracy while improving measurement fitting consistency and computational stability. Furthermore, the proposed tracking mechanism demonstrates bounded execution time variability and enhanced robustness in real-time scenarios. The findings confirm that Koopmanbased tracking provides a viable and computationally efficient enhancement for dynamic state estimation in power systems.

Adaptive Prescribed-Time Tracking Control for Output-Constrained Hydraulic Servo Systems with Time-Varying Parameters

This paper proposes an adaptive prescribed-time tracking control strategy based on the dynamic surface technique for hydraulic servo systems subject to time-varying parameters, external disturbances, and output constraints. First, a state-constrained transformation function is introduced to convert the strict output constraint condition into an error boundedness problem. Meanwhile, the dynamic surface control (DSC) technique is employed to effectively avoid the “explosion of complexity” inherent in traditional backstepping design. Second, to tackle complex uncertainties, prescribed-time-driven adaptive update and disturbance estimation laws are separately formulated for precise parameter learning and active disturbance feedforward compensation. Furthermore, a smooth nonlinear robust term is specifically integrated to suppress the residual errors induced by parameter adaptation. Based on the transformed system, a novel control framework integrating error constraints, adaptive parameter estimation, and prescribed-time performance is developed. Rigorous Lyapunov stability analysis proves that the proposed controller not only strictly prevents the system output from violating the constraint boundaries throughout the entire operation, but also ensures that the tracking error converges rapidly and smoothly to a small bounded region near the origin within a time that can be independently predetermined by the designer. Finally, the effectiveness and superiority of the proposed control strategy are fully validated through simulations.

Model-free predictive control for PMSM based on a nonlinear autoregressive exogenous model and an adaptive recursive least squares algorithm

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.