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Four-switch three-phase inverter (FSTPI) is widely applied as a fault-tolerant topology of six-switch three-phase inverter with an open-phase fault or as a cost-effective topology due to the reduced semiconductor power switches. However, conventional single-vector-based predictive torque control for FSTPI-fed induction motor (IM) presents poor steady-state performance and requires high sampling frequency. To address these issues, a dual-vector-based PTC (DV-PTC) for FSTPI-fed IM with adaptive switching instant is proposed in this article. Therein, two optimal voltage vectors are determined in each sampling period, and a simplification strategy for selecting the optimal voltage vectors is established. A switching instant adaptation scheme is developed to improve the steady-state performance of machine control. An optimized overmodulation strategy is proposed to improve the transient-state performance of machine control and reduce the computational burden in digital implementation. Extensive experimental studies validate the effectiveness and superiority of the proposed DV-PTC scheme.

This article investigates the problem of event-triggered distributed model predictive control (DMPC) for continuous-time nonlinear multiagent systems (MASs) subject to bounded disturbances, input and communication delays simultaneously. The compensation schemes for input and communication delays are proposed, respectively. A new optimal control problem (OCP) to achieve consensus among all agents is formulated under the proposed delay compensation schemes. To alleviate the communication burden and sensing cost, a novel input delay-related periodic event-triggering condition is proposed based on the state error to determine when to calculate the new control input. Sufficient condition guaranteeing the feasibility of the OCP is achieved with the proposed event-triggered control scheme. In stability analysis, a novel time-varying Lyapunov function is constructed, and the input-to-state practical stability of the MASs is derived by using the quasi-Lipschitz continuous property of the Lyapunov function. Numerical results related to vehicle platooning demonstrate the effectiveness of the proposed event-triggered DMPC.

The increasing utilization of wind energy necessitates dedicated attentions to identify vulnerabilities, raise awareness, and formulate strategies for cybersecurity defense, response, and future protection. As a potential cyber attack, denial-of-service (DoS) attacks that block the communication between the rotor speed sensor and the controller of the wind turbine may overload the drive-train and reduce the power generation efficiency. This paper attempts to propose a dual-triggered resilient torque control strategy for the variable-speed wind turbine (VSWT) against DoS attacks. Therein, the mathematical models including the dynamics of the WT and the DoS attack are established, and a neural network observer is designed for the WT system to compensate for the signal losses caused by DoS attack. To guarantee the optimal rotor speed tracking of the WT, an adaptive torque control method based on dual-triggered mechanism is developed, and the attack tolerance capability of the WT system is evaluated. The proposed control strategy can largely enhance the attack resilience capability of the WT, while saving the resources of computation and communication. Extensive studies on a 1.5 MW WT illustrate that the proposed adaptive resilient control scheme can effectively mitigate the impact of the DoS attacks and guarantee the optimal rotor speed tracking performance for the WT system.