Predictive error-dynamics optimal control for PMSM drives with disturbance-augmented Kalman filtering

Active suspension predictive control for high-speed trains based on disturbance feedforward and neural network residual compensation

In high-speed train active suspensions, unmodeled dynamics, wheel-rail wear, and track disturbances can cause dynamic mismatches that degrade the performance of model-dependent predictive control. This study proposes a composite model predictive control approach that integrates neural network residual compensation and equivalent track disturbance feedforward (NNRC-ETDF-MPC) to address this issue. The method firstly estimates the unknown equivalent track disturbances online using an augmented Kalman filter (AKF) and incorporated into the control framework as a physical feedforward compensation term. The nonlinear dynamic residuals are predicted and compensated online using a history-aware physical residual neural network (HA-PRNN), which uses a sliding window to extract multivariate coupling features. Based on these components, a dual feedforward composite control system is developed, along with a constraint-tightening mechanism to prevent actuator saturation. Finally, the performance of the proposed control strategy is evaluated using a co-simulation platform that includes two track spectra, a wide speed range, and cross-service wear conditions. Comparative tests show that the composite control strategy enhances the dynamic vibration suppression capabilities of high-speed trains working in complex, time-varying environments and enhancing robustness against model mismatch. Specifically, under worn wheel conditions, the proposed NNRC-ETDF-MPC achieves average Sperling index improvements of 22.81% and 23.48% across different track excitations.

Asymptotic Tracking Control of PMSM with Full-State Constraints via Adaptive Event-Triggered Nussbaum-Based Design

Hybrid model predictive control and double integral backstepping for speed control of permanent magnet synchronous motors

A robust hybrid control approach integrating continuous control set model predictive control (CCS-MPC) with double integral backstepping (DIBC) is proposed for high-precision speed regulation of permanent magnet synchronous motors. The outer speed loop uses a DIBC controller that generates the reference q-axis current while achieving asymptotic ramp tracking (Type-2 servo performance) without requiring load torque observers. The inner current loop employs CCS-MPC with embedded integral action for optimal q-axis current tracking, while backstepping handles d-axis regulation. Stability is proven using Lyapunov theory, singular perturbation theory, and input-to-state stability arguments. Compared to conventional integral backstepping as baseline, MATLAB simulations (at 10 kHz sampling) demonstrate 79% reduction in speed overshoot, 30% improvement in load rejection, and 11.8× lower integrated absolute error under ramp disturbance, with a computational cost of only 52 FLOPs per sampling period. A quantitative comparison with PI-based field-oriented control and active disturbance rejection control under identical conditions, including random, periodic, and noise-corrupted load scenarios, shows that the proposed controller is the only one of the four that eliminates the steady-state speed error under ramp load disturbances, while requiring no disturbance observer.

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.

Temporal-Feature-Enhanced Reinforcement Learning Control with Adaptive Speed-Loop Gain for PMSM Drives

The high-performance control of permanent magnet synchronous motor (PMSM) drives is challenged by operating uncertainties and coupled electromechanical dynamics. This paper proposes a temporal-feature-enhanced reinforcement learning control approach for a PMSM drive system with adaptive regulation of the speed-loop gain. The learning agent is integrated into a dual-loop control structure to generate voltage control commands and update the speed-loop gain in real time. A multiobjective learning criterion is designed to balance tracking accuracy, torque smoothness, and gain boundedness. Temporal error features are further embedded in the state representation to capture short-term transient trends with limited additional complexity. Comparative simulations with conventional PI control and standard TD3 indicate that the proposed method improves transient response, reduces speed tracking error, and suppresses torque ripple. These results demonstrate the effectiveness of temporal-feature-enhanced reinforcement learning for coordinated control of PMSM drives.

Fixed-time prescribed-performance path tracking control for intelligent vehicles based on adaptive neural network disturbance estimation

Intelligent vehicle path tracking is challenged by uncertain disturbances, such as modeling inaccuracies and external environmental influences, which will significantly compromise both the path tracking accuracy and stability. To address this, this paper proposes a fixed-time prescribed-performance (FTPP) path tracking control method based on adaptive neural network disturbance estimation. Firstly, a radial basis function neural network with an online-updated adaptive law is developed for real-time estimation of uncertain disturbances, effectively compensating for their impact within the control model. Subsequently, a backstepping controller with FTPP is designed by integrating a composite dynamic surface control method with finite-time control techniques. This approach not only enhances the system convergence rate but also mitigates the derivative explosion problem inherent in traditional backstepping, yielding a control law with adaptive disturbances compensation for precise steering control. Finally, based on Lyapunov stability analysis, the boundedness of the closed-loop signals is established under the given assumptions, and the lateral path tracking error is shown to remain within the prescribed-performance bounds under feasible initial conditions. CarSim-Simulink-based co-simulation results validate the effectiveness of the proposed control method in improving both path tracking accuracy and stability.