In this paper, offline adaptive control of a microgrid in an islanded operation mode is presented. The proposed control scheme consists of a power controller, voltage controller, and current controller, which are operating in a cascade structure. The power controller is designed based on the droop characteristics while the voltage and current loops are optimal Proportional-Integral (PI) controllers. Due to the fact that the performance of all PI controllers fundamentally depends on the proper choice of controller parameters, these parameters are optimized based on the offline utilization of the Model Reference Adaptive Control (MRAC) method and the Genetic Algorithm. The adaptation mechanism in the MRAC method is usually determined using the mathematical model of the system, while in this paper, an optimal adaptation mechanism is achieved by the Genetic Algorithm. The optimal parameters of the controller could be obtained based on the measurements of the Distributed Generation (DG) output voltage when the DG is operating with a test load without any knowledge of its model. To investigate the performance of the suggested controller, simulations are done by MATLAB/Simulink in three different scenarios including load perturbation, disconnection of a DG, and nonlinear load, where the results are compared with IEEE recommendation. In addition, the suggested controller is compared with the H2/H∞ robust controller.

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This paper presents the formation tracking problem for non-holonomic automated guided vehicles. Specifically, we focus on a decentralized leader–follower approach using linear quadratic regulator control. We study the impact of communication packet loss—containing the position of the leader—on the performance of the presented formation control scheme. The simulation results indicate that packet loss degrades the formation control performance. In order to improve the control performance under packet loss, we propose the use of a long short-term memory neural network to predict the position of the leader by the followers in the event of packet loss. The proposed scheme is compared with two other prediction methods, namely, memory consensus protocol and gated recurrent unit. The simulation results demonstrate the efficiency of the long short-term memory in packet loss compensation in comparison with memory consensus protocol and gated recurrent unit.

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Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.