In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring MGs and the main grid in the real-time energy market. Various uncertainties including renewable generation, load consumption, and buying/selling prices of the main grid are handled using an adjustable robust optimization technique. To keep consistent with the distributed nature of the multiple MGs, we propose a distributed adjustable robust optimal scheduling algorithm (DAROSA) to solve the energy management problem. Via distributed communication of non-critical information with the neighboring MGs, each MG energy management system optimizes its own energy selling price and operation schedule to minimize its worst-case operational cost under a certain robustness level. Robust optimal scheduling and fair energy trading can be collectively achieved. A case study of a 4-MG system is conducted to validate the effectiveness of the proposed approach.

Sliding mode control is widely used to enhance the speed control performance of permanent magnet synchronous motors (PMSM). However, the slow reaching onto the sliding surface and chatting phenomena of conventional sliding mode reaching law (SMRL) is still a challenging issue. This article proposes a novel adaptive SMRL (ASMRL) to solve these problems. This ASMRL includes the system state variables, an exponential function term bounded by the absolute value of the switching function, and an adaptive switching gain term influenced by the convergence of the system state. This approach efficiently accelerates the velocity at which the system state reaches the sliding mode surface and effectively suppresses chattering. Then an enhanced sliding mode controller is designed for the speed regulation of PMSM systems. Finally, mathematical stability analysis, numerical analysis, simulation, and experimental validation are conducted to demonstrate the effectiveness of the proposed method.

The complex cyber layer challenges the accuracy of cooperative tracking motion for the networked multiple linear motors system. This article focuses on model predictive control (MPC) for networked control system with the denial-of-service (DoS) attack and network-induced time delay. In order to improve tracking accuracy and control performance of the system, several topologies for linear motion models are studied. The DoS attack and time delay are modeled as constraint models in the time domain. Combining dynamic prediction horizon with the buffer compensation in the controller to actuator (C-A) channel, the proposed networked control strategy based on MPC effectively solves the impact of DoS attacks and time delays on the tracking performance of multiple linear motors. In order to ensure the recursive feasibility of the MPC optimization problem and the uniform global asymptotic stability of system, the output terminal constraint and dynamic prediction horizon constraint are considered by combining four DoS attack cases of the networked control strategy, and a Lyapunov function is constructed. Finally, three linear motors with different topologies are built, and the simulation results verify the control effectiveness of the proposed control strategy on the motor tracking performance under cyber constraints.

Friction deteriorates speed and position control accuracy of high-precision machine tools and causes distortion phenomenon when the speed crosses zero. In this article, a new compensation method based on static Stribeck (SB) model combined with fuzzy low-pass filter (SBFF) is studied for friction compensation of permanent magnet synchronous motor servo systems. First, models of the servo system, the SB and the LG, are established. Then, it is theoretically derived that the hysteresis effect of the LuGre (LG) model can be equivalent to a low-pass filter term compared to the SB model. Based on this, the relationship between the filter time constant and the motor reference speed can be derived. To further improve the friction compensation effect near zero speed and to weaken the delay generated by the filter during the non-zero-speed range, fuzzy logic is introduced to adjust the filter time constant according to the reference speed variation. Through simulations and experiments, compensation strategies based on the LG feedforward, SB feedforward, SBFF feedforward, and SBFF feedback are compared, and the robustness of the proposed SBFF feedforward compensation method over entire speed range is verified as well.

In a multi-microgrid system (MMG), the microgrids (MGs) are normally managed by independent operators. Distributed energy trading/scheduling schemes via interactions of these MG operators have been extensively investigated. How to coordinate these MGs to implement the acquired optimal schedule in the real time under constant load fluctuation while guaranteeing operational stability is seldom reported. Due to the intrinsic advantages of scalability, robustness, and fast response in comparison to the centralized scheme, a multi-agent based distributed optimal tie-line power flow control strategy is proposed to achieve this objective, which is facilitated by a regional communication network overlapping each MG and distributed sensors monitoring the tie-line power flows. When the MMG is operated in grid-connected mode, the proposed scheme can maintain the scheduled tie-line power flows among the MGs in the presence of any disturbance by adjusting the real-time power outputs of the distributed energy resources (DERs) proportionally. When the MMG is islanded, frequency recovery can be further achieved via a local frequency feedback mechanism. Convergence of the proposed approach is analytically proved. Simulation results in a 4-MG system modified from the IEEE 34-bus test feeder system validate the effectiveness and efficiency of the proposed approach in both grid-connected and islanded modes.