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The in-wheel electric vehicle with distributed drive units has better stability and flexibility than traditional centralized drives, but may encounter a higher failure rate due to additional actuators and sensors, especially that the faults of the wheel-side position sensor make motor torque out of control. To overcome this problem, a fault-tolerant control strategy with a multi-states switching method is proposed. The strategy judges the sensor failure by verifying redundant speed information, realizes sensorless control schemes by flux-observer based algorithm in high-speed range and I-F control algorithm in low-speed range with low acoustic noise, and applies adaptive transition process between different control schemes. To pursuit high stability, the signal-to-noise analysis for fault judgment due to sensorless estimation accuracy is discussed. Meanwhile, the principle of I-F resonant oscillations during the transition process is initially deduced in detail, and the conclusion of stability condition is obtained. Finally, the influence of system parameters on resonance performance is analyzed by simulation, and the effectiveness and reliability of the proposed strategy for the risk-controlling process are verified by experiments.

The precise information of initial rotor position is very important to high performance Interior Permanent Magnet Synchronous Machine (IPMSM) adopting incremental encoder, which can normally be obtained by the high-frequency (HF) rotating or pulsating voltage injection methods. However, the demodulation procedure may confront with the challenge of constrained stability and limited accuracy. For improving estimation accuracy, an improved impulse injection method with merits of simple and fast is proposed. It is carried out by generating a series of phase-axial injections to calculate the probable rotor position, then injecting two reciprocal voltage pulses to obtain the rotor polarity, and finally injecting iterative voltage vectors to obtain the real position rapidly. During the estimation process, the filters to extract the high frequency current signals are not needed. The method effectiveness is validated by the measured results of IPMSM test platform. In the same time, its application limitation is deduced by comparison with the measured results between IPMSM and surface-mounted PMSM (SPMSM). It is shown that the estimation algorithm is compatible with motor parameter differences and can reduce the influence of inductance saturation and nonlinear voltage error. Therefore, the proposed method not only improves the accuracy and robustness of the PMSM sensorless startup control, but also ensures the fast response.

Kinematic Calibration is an effective and economical way to improve the accuracy of the six degree-of-freedom (DoF) parallel kinematic machine (PKM), named as Stewart platform, for the large component assembly in aviation or aerospace. The conventional online calibration requires a powerful and complicated control system, whereas the current offline calibration methods are not satisfactory in terms of the compromise between efficiency and accuracy. This paper proposes a semi-online calibration method in which the geometric errors are identified offline and compensated online. The geometric errors are inserted into the inverse kinematic model. Instead of formulating the linear mapping model between geometric errors and the pose error of moving platform, the error model is written as the function of geometric errors with respect to the actuation inputs. Hence, a nonlinear error model is obtained. Without worrying about the identifiability, the error identification equations are converted into an optimization problem and solved by the hybrid genetic algorithm (HGA). In the traditional offline compensation, the identified kinematic parameters are adopted to modify the nominal kinematic model, which is inconvenient when the control system is not transparent to the users. A new control block that calculating the equivalent actuation inputs from the identified errors is added to the control flow. The errors are compensated in an efficient manner. Simulations and experiments are implemented to validate the accuracy, efficiency and convenience of the proposed method. The results indicate that our approach improves position and orientation accuracy of the Stewart platform by 85.1% and 91.0%.

Memtransistor, a hybrid structure that integrates the function of memristor and transistor, is a promising device prototype for the realization of complex neuromorphic learning owing to its diverse functionality and additional flexibility in emulating synaptic behaviors. Memtransistor of two-dimensional (2D) chalcogenide materials have received many interests as it has distinctive memristive mechanism quite different from conventional oxide memristors. Here, we report a memtransistor based on the two-dimensional thin films (2DTFs) of non-layered β-In2S3. The In2S3 2DTFs grown by physical vapor deposition method have microscopically visible grain boundaries (GBs) formed by the stacking and interconnecting of 2D In2S3 flakes. The memtransistors of In2S3 2DTFs show tunable bipolar resistive states with resistance ratio up to 105, endurance over 200 cycles, and a retention time of 104 s. Illumination of laser light from visible and near-infrared are able to induce intermediate resistance states in memtransistors, enabling optical-modulated multilevel memory storage. Also, the memtransistors are able to emulate the synaptic function of long-term potentiation (LTP) and long-term depression (LTD) with tunable synaptic weight in response to presynaptic stimuli of drain/gate pulses. Interestingly, the plasticity of LTP and LTD behavior can be switched in a highly tunable manner by simply varying the gate voltages. The diverse optoelectronic properties and controllable functionality of memtransistors based on the emerging 2D In2S3 offer a useful guide to potential application in electronic memory and artificial synapses.

Smart home scheduling, facilitated by advanced metering, monitoring, and manipulation technology, plays an important role in the energy transition in terms of accommodating intermittent renewable energy and improving energy consumption efficiency. The key functionalities of home energy scheduling are usually implemented by leveraging the flexibility of household appliances, such as thermostatically controlled loads (TCLs) and energy storage units, to improve the peak-to-average ratio for utilities and reduce energy bills for customers. However, the consumption patterns of appliances are greatly influenced by a variety of factors, including real-time tariffs, ambient temperature profiles, indoor activities, and solar irradiance. Hence, smart home energy scheduling is a challenging task because most of these impacting factors are stochastic and difficult to predict. To properly model and manage the uncertainty factors associated with smart home appliance scheduling, an economic model predictive control (MPC)-based bilevel smart scheduling scheme is proposed in this paper. The comprehensive modeling of distributed generation and household appliances is performed at the single-household level. The home energy scheduling problem is formulated on two levels, with the upper level emphasizing the economic impact and the lower level focusing on capturing TCL responses. The correlations among different TCLs and their performance under the influence of various uncertainty factors, such as environmental impacts and user behaviors, are considered. The efficiency of the proposed MPC-based bilevel optimization model and the effectiveness of the home energy scheduling strategy in managing uncertainties are validated and illustrated in numerical studies.

Increasing deployment of distributed energy resources (DERs) is re-sculpturing the modern power systems in recent years. Future smart power distribution systems should be competent at accommodating extensive integration of DERs and managing the associated uncertainties at the distribution level. The electricity market has been proved to be an efficient way to employ market signals to direct behaviors of users and DERs with large capacity and homogeneous pattern. However, existing market frameworks cannot effectively handle a large number of small-scale DERs due to their diverse characteristics and arbitrary behavior patterns. In this context, an aggregated model which can represent and manage a diverse collection of DER, load, and storage is proposed. An additional trading platform, namely the energy sharing market, is established to reinforce the coordination and collaboration among various aggregators as well as operators. Energy sharing scheme is applied and a corresponding dynamic dispatch platform is designed to solve the crowdsource problem. The efficiency of the proposed model is validated by the numerical studies, and the market performance and impacts of energy sharing on the power systems are illustrated.