Xia Hua

This paper presents an electromechanical coupling model integrating an equivalent magnetic network (EMN) model of a dual three-phase permanent magnet synchronous motor (DTP-PMSM) with the dynamic model of a helical planetary gear transmission system. Using this model, this study analyzes the dynamic characteristics of an electric drive system, specifically motor phase current, electromagnetic torque, and gear meshing force, under self-fault-tolerant control strategies. Simulation and experimental results demonstrate that the self-fault-tolerant control strategy enables rapid fault tolerance during open-phase faults, significantly reducing system fault recovery time. Meanwhile, compared to the open-phase faults conditions, the self-fault-tolerant control effectively suppresses most harmonic components within the system; only the second harmonic amplitude of the electromagnetic torque exhibited an increase. This harmonic disturbance propagates to the gear system through electromechanical coupling, synchronously amplifying the second harmonic amplitude in the gear system’s vibration response. This study demonstrates that self-fault-tolerant control strategies significantly enhance the dynamic response performance of the electric drive system under open-phase faults conditions. Furthermore, this study also investigates the electromechanical coupling mechanism through which harmonics generated by this strategy affect the gear system’s dynamic response, providing theoretical support for co-optimization electromechanical coupling design and fault-tolerant control in high-reliability electric drive transmission systems.

This article proposes an adaptive spatial repetitive learning control scheme to achieve high-precision tracking of aperiodic speed trajectories with predefined performance for permanent magnet synchronous motor (PMSM) systems. The position-related periodic uncertainty induced by the angular position is accurately compensated through constructing a fully saturated spatial repetitive learning law, and thus the high-precision tracking for aperiodic desired trajectories can be achieved. For guaranteeing the predefined performance of the speed tracking error convergence, a predefined-position control law is designed to drive the tracking error to converge into a small region near the origin within the predefined position. The experimental studies on a three-phase PMSM setup are provided to corroborate the theoretical results. It is shown that the proposed scheme can enable the predefined-position convergence of the speed tracking error, and the steady-state accuracy is further improved under the effect of spatial repetitive learning law, so as to improve the speed tracking performance of the PMSM system effectively.

To enhance the balance between lateral stability and energy efficiency, we propose an adaptive compound controller based on phase plane analysis for four-wheel independent drive electric vehicles (4WID-EVs). The adaptive stability and energy-saving controller (SEC) is designed with a three-layer structure. The upper-layer controller employs model predictive control (MPC) to compute the external yaw moment based on the desired yaw rate and side slip angle derived from a reference model. The adaptive-layer controller utilizes a phase plane diagram to evaluate vehicle stability and reduces unnecessary external yaw moment consumption by accounting for the vehicle’s steering state and battery’s state-of-charge (SOC) level. The lower-layer controller implements an optimal torque distribution algorithm to minimize an objective function that considers tire workload, energy consumption, and smooth motor control. Numerical simulations are performed in MATLAB/Simulink using three distinct steering angles to evaluate the performance of the proposed control strategy. At each steering angle, the SEC’s stability and energy efficiency are compared to those of the energy-saving controller (EC) and stability controller (SC) under varying battery charge levels. The results indicate that, at small steering angles, the vehicle operates in a highly stable state, enabling a reduction in the external yaw moment to achieve substantial energy savings. As the steering angle increases, the vehicle approaches a critical stability state, where the external yaw moment is applied to maintain lateral stability. Furthermore, as the SOC decreases, the SEC strategy will increasingly prioritize energy savings. Simulation results verify that the SEC strategy effectively balances lateral stability and energy savings while maintaining consistent performance across a range of operating conditions.

Dynamic path planning enables vehicles to autonomously navigate in unknown or continuously changing environments, thereby reducing reliance on fixed maps. Deep reinforcement learning (DRL), with its superior performance in handling high-dimensional state spaces and complex dynamic environments, has been widely applied to dynamic path planning. Traditional DRL methods are prone to capturing unnecessary noise information and irrelevant features during the training process, leading to instability and decreased adaptability of models in complex dynamic environments. To address this challenge, we propose a dynamic path-planning method based on our Causal State-Masking Twin-delayed Deep Deterministic Policy Gradient (CSM-TD3) algorithm. CSM-TD3 integrates a causal inference mechanism by introducing dynamic state masks and intervention mechanisms, allowing the policy network to focus on genuine causal features for decision optimization and thereby enhancing the convergence speed and generalization capabilities of the agent. Furthermore, causal state-masking DRL allows the system to learn the optimal mask configurations through backpropagation, enabling the model to adaptively adjust the causal features of interest. Extensive experimental results demonstrate that this method significantly enhances the convergence of the TD3 algorithm and effectively improves its performance in dynamic path planning.

The nonlinear mechanical properties of ballasted tracks along railways result in complex dynamics of the vehicle–track systems. Employing localized characterization of ballast and a simplified model might underestimate the vehicle–track system’s dynamical responses and safety. This paper presents a new dynamical stiffness model of railway ballast by incorporating the ballast’s time-varying, nonlinear viscoelastic, and elastoplastic properties. The new nonlinear stiffness model is a versatile tool that comprehensively characterizes the ballast characteristics of displacement-dependent stiffness, frequency-dependent stiffness, hysteresis, and time/space-varying features. These features, widely reported in previous experimental research, can now be effectively understood. Conventionally, to characterize time/space-varying characteristics of ballast along the track, ground-penetrating radar (GPR) has been used as the most efficient approach to survey railway networks quickly and to infer track dynamical properties. Aiming to improve the present technique for characterizing time/space-varying properties of ballast stiffness by using a GPR signal, the adaptive optimal kernel time–frequency representation (AOKTFR) method is used to process a typical GPR signal from a railway ballast scanning. It is found that the results of AOKTFR exhibit a clear time-varying pattern and precise frequency modulation. In contrast, the conventional time–frequency methods failed to give a clear time-varying pattern. The results illustrate that AOKTFR is a practical approach for processing the time-varying nonlinear signal of GPR and correlating it with the time-varying nonlinear stiffness of ballast.