Wireless power transfer (WPT) has emerged as a key enabling technology for the large-scale adoption of electric vehicles (EVs), offering enhanced charging flexibility, improved safety, and seamless integration with intelligent transportation and renewable energy infrastructures. This paper presents a comprehensive review and technical synthesis of WPT technologies spanning both near-field and far-field domains, including inductive power transfer (IPT), magnetically coupled resonant WPT (MCR-WPT), capacitive power transfer (CPT), microwave power transfer (MPT), and laser wireless charging (LPT). Particular emphasis is placed on MCR-WPT, the most widely adopted approach for EV wireless charging, for which the coupler structures, resonant compensation networks, power converter architectures, and control strategies are systematically analyzed. The review further identifies that hybrid WPT architectures, adaptive compensation design and wide-coverage coupling mechanisms will be central to enabling high-power, long-distance, and misalignment-resilient wireless charging solutions for next-generation electric transportation systems.

The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control strategy that integrates proportional–integral (PI) control with a deep Q-network (DQN). The proposed framework leverages the advantages of PI control in terms of steady-state regulation and a fast transient response, while also exploiting the capabilities of the DQN agent to learn optimal control policies in dynamic and uncertain environments. To validate the effectiveness and robustness of the proposed hybrid control framework, a detailed boost converter model was developed in the MATLAB 2024/Simulink environment. The simulation results demonstrate that the proposed framework exhibits a significantly faster transient response and enhanced robustness against nonlinear disturbances compared to the conventional PI and fuzzy controllers. Moreover, by incorporating PI-based fine-tuning in the steady-state phase, the framework effectively compensates for the control precision limitations caused by the discrete action space of the DQN algorithm, thereby achieving high-accuracy voltage regulation without relying on an explicit system model.

The constant current followed by constant voltage (CC-CV) charging method is commonly employed for battery charging, effectively extending battery life and reducing charging time. However, as charging progresses, the battery’s internal resistance increases, complicating the charging circuit. This paper designs a wireless power transfer system utilizing an LCL coupling network for battery charging. The system employs frequency modulation (FM) to manage its CC and CV output characteristics at two fixed frequency points. The influence of the LCL coupling network parameters on system output characteristics was investigated using MATLAB. A simulation model was developed in the PSIM environment to validate the CC and CV output characteristics. The simulation results show that the system has load-independent constant-current and constant-voltage characteristics at two different frequency points. In order to verify the theoretical analysis, an experimental platform was also established. Experimental results demonstrate that the proposed system operates effectively in CC mode, maintaining constant output current across various loads, while in CV mode, it effectively regulates output voltage for different loads. The designed frequency modulation controller ensures a rapid response to sudden changes in load resistance, regardless of operating in CC or CV modes.

This study proposes an adaptive fault-tolerant control strategy for parametric strict-feedback systems subject to actuator faults and unknown dead-zone nonlinearities, a combination that presents significant challenges for controller design. First, a novel prescribed-performance fault-tolerant control framework is developed by incorporating a funnel function, a barrier Lyapunov function, and a bounded estimation mechanism to address the issue of multiple constrained nonlinear disturbances. Second, the proposed strategy offers two key improvements: (1) adequate compensation for the coupled effects of actuator faults and dead-zone nonlinearities, and (2) guaranteed globally prescribed transient performance, making the settling time and tracking accuracy independent of initial conditions and design parameters. Lastly, simulation results verify the approach’s effectiveness, showing rapid convergence within 0.8 s and a tracking error bounded by $\pm 0.05$, thus surpassing traditional methods.

In this paper, an adaptive output feedback control scheme is proposed for a class of parametric strict feedback systems with asymmetric full-state constraints and unknown output gain. Firstly, an adaptive state observer is constructed to estimate the unmeasured system states. To compensate for the effect of the unknown output gain on the tracking performance, a new error signal incorporating an adaptive compensation coefficient is introduced into the backstepping design. Then, by combining the universal transformed function with a coordinate transformation, all system states are kept within time-varying asymmetric bounds, and the feasibility issues of conventional constrained control methods are avoided. Based on Lyapunov stability analysis, all signals in the closed-loop system are proven to be globally uniformly ultimately bounded. Finally, simulation results based on motor models demonstrate the effectiveness of the proposed scheme.

This article focuses on the event-based adaptive distributed optimization problem of uncertain complex multiagent networks subject to unmodeled nonlinearities, uncertain disturbances, and unknown interaction direction. The measured gradient functions depending on the real output information of multi-agent systems (MASs) are employed, rather than explicit gradient information of the local objective functions. To reduce continuous communication and update requirements, an adaptive dynamic event-triggered strategy is applied. Under these multiple constraints, achieving asymptotic optimization in MASs presents a significant challenge. To this end, a novel event-based distributed optimization scheme is developed, incorporating a feedback tracking term, a neural network estimation term, and a disturbance compensating term. The fuzzy neural networks are used to approximate the nonlinear functions. Based on this framework, a novel adaptive distributed optimization controller is constructed to ensure optimal consensus, whose stability and convergence are rigorously established via Lyapunov-based analysis. Furthermore, the proposed neuroadaptive distributed optimization algorithm is extended to the practical classes of systems with unknown virtual control coefficients. Lastly, the proposed neuroadaptive distributed optimization scheme is illustrated using two examples.