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In this article, a converter-based multibus dc microgrid (MG) in series is studied, in which the tradeoff between voltage recovery and current equalization has been a hot topic of interest. To solve this problem, a distributed learning-based high-order fully actuated (DL-HOFA) secondary control is proposed in this article, and the simple structure of this control technique facilitates its application in various modern dc MGs with different configurations. Before designing the secondary control protocol, this article provides a comprehensive description of dc MGs in advance. In the suggested control strategy, the controller design is tightly related to the underlying physical characteristics of the MG, and this prominent feature represents a significant improvement in its adaptability. In addition, the DL-HOFA control obtains fast dynamic and accurate current sharing performance by virtue of the high-order fully actuated dc MG model. The effectiveness of the proposed control method is verified on a real photovoltaic- and battery-based hardware system with maximum power point tracking controller.

This article is concerned with the problem of resilient output tracking control for a class of discrete-time linear cyber-physical systems (CPSs) subject to denial-of-service attacks. The remote information exchange between the sensor and controller in CPSs employs a multichannel network because of its inherent advantages of resistance to destruction as well as to attacks. In order to design defense strategies to mitigate the negative impact of network attacks on the tracking performance of CPSs, considering the goals and energy constraints of both sides, the interaction process between them is modeled as a zero-sum stochastic game. Then, the Nash equilibrium of the problem is found with the help of the proposed Q-learning algorithm through an artfully designed state-based adaptive one-stage reward function. On the basis of the obtained optimal mixed strategy, the resilient output tracking controller is designed. Different from the work focusing on remote state estimation by means of game theory, this study makes the first effort to design a resilient output tracking control algorithm for CPSs with multiple channels using a stochastic game approach. Finally, the effectiveness of the proposed resilient controller with the optimal defense strategy is validated on a renewable energy generation system prototype by extensive case studies.

This article contributes to the realization of a unified and flexible framework that can cover whole processes of a control system for learner-centered learning scenarios. It introduces the application of a pocket laboratory with a unified and flexible framework to engineering education at Wuhan University. The pocket laboratory, named Mathematics to Practice Laboratory (M2PLab), is successfully designed and implemented for experimental courses. Different from conventional pocket laboratories, the M2PLab system has advantages, such as web-based plug-in free access, graphical algorithm design, simulation, code generation, and monitor integration just to name a few. Considering the hardware cost, size, and computing performance, the M2PLab system is deployed into the hardware of Raspberry Pi. Due to the small size of Raspberry Pi, the pocket laboratory can be carried into student dormitories or somewhere else to conduct experiments at any time. In this article, a pocket laboratory package including a Raspberry Pi, a compact test rig and cables is assigned to each student to finish experiments of Automatic Control Theory course. After the experiments, students were asked to fill out a satisfaction survey about the experience of the use of the pocket laboratory. Students' feedback verifies the effectiveness of the pocket laboratory.