Cast-resin type current transformers are widely used in power systems due to their good insulation, light weight and high reliability. Compared with the oil-immersed current transformer, the external electric field st...
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The large-scale grid connection of photovoltaic (PV) systems creates great hazards to the operation of power equipment and has a great impact on the stability and reliability of power systems. To achieve stable contro...
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Addressing the problem that the pulse component often submerges in other frequency components during the failure of the wind turbine gearbox, it is difficult to extract effectively the early fault characteristics. Thi...
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Based on time series data collected by the ZPW-2000A track circuit detection vehicle, which detects the induced voltage of compensating capacitor, this paper presents a method for predicting compensating capacitor fau...
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The event-triggered control problem of switched nonlinear cyber-physical systems (CPSs) is investigated under average dwell time (ADT). A new dynamic gain is proposed, which can effectively compensate for the unknown ...
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In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main fact...
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Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear *** this work,a novel twolayer reinforcement learning behavioral contro...
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Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear *** this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error ***,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission *** with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural *** the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control *** with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and *** error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.
This paper presents a novel circular tablet defect detection algorithm based on machine learning techniques to address the challenge of insufficient data for training samples. Utilizing cameras, lenses, and light sour...
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This paper proposes an innovative vector control method for single-phase differential Buck inverters, advancing beyond the limitations of traditional control strategies. Unlike previous methods that require a T/4 dela...
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In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at prese...
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