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

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

ADAPTIVE NEURAL NETWORK-ASSISTED LINEAR QUADRATIC REGULATOR CONTROL WITH FULL-ORDER EXTENDED STATE OBSERVER FOR ACTIVE SUSPENSION SYSTEMS

This paper proposes integrating a Linear Quadratic Regulator (LQR) control scheme with an adaptive Neural Network (NN) updating law to improve ride comfort performance. The dynamic states of the suspension system are effectively estimated using a Full-Order Extended State Observer (FOESO) rather than through direct sensor measurements, thereby reducing implementation costs and minimizing the influence of sensor noise. Numerical simulations are conducted under two different road profile scenarios based on ISO standards, while accounting for lumped uncertainties in the system dynamic modeling. The results show that the Root Mean Square (RMS) body displacement is reduced to 4.854 mm in the first case with ISO C-class road disturbance, and the RMS body acceleration decreases to 0.220 m/s² in the second case with ISO D-class road disturbance; both are significantly lower than those achieved by conventional controllers. Furthermore, the dynamic states are estimated with high accuracy, confirming the effectiveness of the proposed control strategy in suspension system regulation.

Adaptive speed synchronization of dual-motor drives using a recurrent Type-2 fuzzy NARX-CMAC network

This paper proposes a recurrent Type-2 fuzzy Nonlinear autoregressive networks with exogenous input cerebellar model articulation controller (NARX-CMAC) control strategy for speed synchronization in dual-motor drive systems operating under nonlinear dynamics, external disturbances, and load variations. Precise synchronization between two motors is a critical requirement in many industrial applications, where even small speed deviations may degrade product quality, increase mechanical stress, and reduce system reliability. However, conventional controllers such as PID and linear model-based approaches often show limited performance when the plant is subject to parameter uncertainty, nonlinear effects, and measurement noise. To address these challenges, the proposed method integrates three complementary mechanisms into a unified nonlinear control framework. Type-2 fuzzy inference is employed to enhance uncertainty handling, the NARX captures the temporal behavior of the system and improves dynamic prediction, while the CMAC provides fast local learning and efficient online adaptation. In addition, the recurrent structure enables the controller to exploit past system information, thereby improving transient synchronization and disturbance rejection capability. The experimental results demonstrate that the proposed controller achieves high synchronization accuracy, rapid dynamic response, and stable operation even in the presence of noise and time-varying loads. Compared with conventional control methods, the developed Type-2 fuzzy NARX-CMAC scheme offers superior robustness and shows strong potential for intelligent synchronization control in high-performance multi-drive applications.

A Model Predictive Current Control for Interior PMSM Based on Least Squares Parameter Adaptive Feedback Correction

The model predictive current control (MPCC) of an interior permanent magnet synchronous machine (IPMSM) requires an accurate motor parameter model to predict future currents and achieve high control performance. However, the inductance parameters of an IPMSM are easily affected by factors such as magnetic field saturation, leading to large current prediction errors, high current ripple, and poor stability. Therefore, an MPCC strategy for an IPMSM based on parameter adaptive feedback correction is proposed. First, based on the mathematical model of the IPMSM in the synchronous rotary coordinate, the cross-coupling relationship between the dq-axis inductance deviations and the current prediction error is derived to form an explicit prediction error model. Then, the influence of the d-axis and q-axis inductance parameter deviations of the IPMSM on the current prediction error is discussed in detail. Next, based on the established mathematical model of the prediction error, the recursive least squares scheme is adopted to identify the d-axis and q-axis deviations of the inductance parameters online. Finally, unlike conventional open-loop RLS correction, a PI-based closed-loop correction loop is designed that feeds the prediction error back to adjust the inductance deviations, thereby forcing the prediction error toward zero while inherently compensating for inverter dead-time effects. Simulations and experiments were conducted, and the results show that the proposed scheme greatly improves the accuracy of current prediction and inductance parameter estimation, and enhances robustness against parameter mismatch and dead-time disturbances. The key novelty lies in the PI-feedback-driven RLS closed-loop structure that simultaneously achieves error elimination and dead-time compensation.

Model‐Free Predictive Control of Induction Motor Using a Correntropy Criterion–Based Unscented Kalman Filter

In conventional model predictive control, three‐level inverter‐fed induction motor systems are susceptible to parameter mismatch, leading to degraded control performance. To enhance parametric robustness against nonlinear dynamics and impulsive noise, this paper proposes a model‐free predictive torque control using a correntropy criterion–based unscented Kalman filter (CCUKF). First, an ultralocal model is employed to consolidate system uncertainties into a lumped disturbance. Second, the sigma‐point sampling method of the unscented Kalman filter accurately captures nonlinear statistical characteristics, avoiding the linearization errors inherent in the extended Kalman filter and improving state estimation accuracy. Furthermore, the correntropy criterion is introduced to optimize the Kalman gain, robustly suppressing non‐Gaussian noise and outliers caused by electromagnetic interference. Experimental results demonstrate improvements in both dynamic response and steady‐state performance, along with effective suppression of torque fluctuations, showing superior performance compared with conventional methods while reducing dependence on motor parameters.

Predictive current control strategy for PMSM based on NADRC

Predictive current control (PCC) for permanent magnet synchronous motors (PMSM) exhibits slow response and obvious chattering under parameter variation and load shock, while existing schemes cannot coordinate anti-disturbance performance, dynamic speed and battery power constraints. This paper designs an improved dynamic double-power reaching law (DPRL) with finite-time convergence and low chattering, embeds it into nonlinear active disturbance control (NADRC) coupled with an extended sliding mode disturbance observer, and adds a battery power limiting module. Simulations and dual-motor bench tests are implemented with multiple contrast algorithms and ablation groups. The proposed strategy achieves zero overshoot across all test conditions. During sudden 10 N·m load, the speed drop is only 285 r/min with 1.5 s recovery; acceleration and reversal response time are reduced by 40% and 70% respectively, and d/q‐axis current ripples are significantly weakened. The integrated DPRL‐NADRC PCC enhances PMSM robustness and dynamic performance under complex disturbances and power constraints. Future work will develop automatic gain tuning algorithms and validate the method under high‐speed demagnetization and multi-motor operating scenarios.