This paper proposes a sensorless strategy for detecting the connector engagement state in robotic grippers using the current information of an electromagnetic shaker. In automated connector assembly, accurate insertion and reliable verification of the engagement state are essential, as both directly affect assembly quality and process stability. Conventional approaches typically rely on external sensors, such as vision, position, or torque sensors; however, these methods are often limited by high cost and sensitivity to environmental variations, which can result in sensing failures. To overcome these limitations, the proposed method detects the connector engagement state using only the shaker current, without requiring any additional sensors. As a result, the proposed approach simplifies the overall system structure while improving reliability in automated assembly processes.
An effective control method for suppressing voltage distortion and voltage unbalance in auxiliary power unit (APU) inverters for electric locomotives subjected to nonlinear and unbalanced loads is presented in this paper. To overcome the inherent limitations of hardware-based compensation and the restricted unbalance-compensation capability of conventional αβ reference frame approaches, an abc reference frame-based output-current feedforward scheme is developed. Both PSIM simulations and experiments on a 10 kW prototype demonstrate that the proposed method achieves improved voltage quality over a wide range of operating conditions.
Lithium-ion batteries (LIBs) serve as core energy components in various applications, including new-energy vehicles, unmanned aerial vehicles, and energy-storage systems. In application, precise state-of-health (SOH) estimation is crucial for ensuring safe, reliable operation and improving system efficiency. However, the limitations of traditional SOH estimation methods in terms of interpretability and data generalization remain to be addressed. To tackle these challenges, a solution is proposed using a self-adaptive physics-informed neural network (SA-PINN) approach. The method extracts highly-correlated features from electronic current and temperature profiles during the constant-voltage charging phase while simultaneously incorporating solid electrolyte interphase (SEI) film growth kinetics and the Arrhenius temperature effect as dual physical constraints to guide network learning. A collaborative mechanism combining adaptive weighting and transfer learning enables SOH monitoring across different battery types and operating conditions, enhancing physical consistency without compromising data-fitting accuracy. Experimental results indicate that the proposed method achieves a mean absolute percentage error (MAPE) below 0.81
Addressing the severe torque ripple and output torque loss in five-phase interior permanent magnet synchronous motors (FP-IPMSMs) during single-phase open-circuit faults, a novel virtual signal injection (VSI)-based maximum torque per ampere (MTPA) control strategy integrated with fault-tolerant current optimization is proposed in this paper. First, the post-fault reduced order decoupling transformation matrix is constructed, and the single-phase open-circuit mathematical model of FP-IPMSM in synchronous rotating coordinate system is obtained. Second, based on the principle of constant magnetomotive force before and after the fault, the current constraint condition for minimizing torque ripple is solved to construct fault-tolerant vector control. On this basis, the fault-tolerant current that maximizes the permanent magnet torque component is solved. Then, the VSI-MTPA approach dynamically exploits the reluctance torque component in salient-pole motors, enabling higher torque density during fault-tolerant operation. Finally, simulation and experimental validation demonstrate that the proposed strategy reduces torque ripple while increasing the output torque under single-phase fault conditions.
The randomness and intermittency of wind speed cause severe fluctuations in the output power of wind farms (WFs). To minimize battery lifetime cost and smooth power output, a coordinated power control (CPC) strategy is proposed for the energy storage systems (ESSs) of WFs based on the model predictive control (MPC). First, to linearize the aerodynamics, drive trains, and energy storage converters of wind turbines, the small-signal analysis method is employed to derive the linear incremental equations of WF-ESSs. Then, to quantify the nonlinear life degradation process of ESSs, a degradation cost function incorporating the coupling relationship between depth of discharge (DOD) and temperature is established. A MPC-based model of wind turbines (WTs) and ESSs is developed to optimize the active power output for minimizing battery lifetime cost and smoothing power fluctuations. Finally, a WFs-ESSs simulations is established to validate the effectiveness of the proposed method.
