In this paper, the event-triggered asynchronous control problem of nonlinear multi-agent systems is investigated based on T-S fuzzy models. In order to rationally utilize network resources and elaborately avoid unnecessary continuous monitoring, an event-triggered mechanism with a new dynamic threshold parameter and a sampler are considered, respectively. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and fuzzy controller. Based on the Lyapunov stability theory and appropriate inequality, some sufficient criteria in the form of linear matrix inequalities are obtained to ensure the stability of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness and superiority of the proposed method.

Distributed control technology has significantly improved the regulation of dc microgrid systems. However, it also introduces potential cyber-security threats during the communication process. In particular, the system is vulnerable to two types of attacks: denial-of-service (DoS) and false data injection (FDI) attacks. If both attacks occur simultaneously, they can destabilize the dc microgrid and jeopardize its safe operation. To ensure system stability under mixed DoS and unbounded FDI attacks, a consensus-based secondary control strategy is proposed. This strategy includes an adaptive feedback term to counteract FDI signals in the presence of DoS attacks. The stability of the system is rigorously analyzed using the Lyapunov method. Finally, numerical and experimental tests confirm the effectiveness of the proposed strategy.

Energy management in the smart home can help reduce residential energy costs by scheduling various energy consumption activities. However, accurately modeling factors, such as user behavior, renewable power generation, weather conditions, and real-time electricity prices can be challenging, making the design of an efficient energy management strategy difficult. This article proposes a real-time energy management algorithm based on deep reinforcement learning (DRL) for smart homes equipped with rooftop photovoltaics, energy storage systems, and smart appliances. The algorithm aims to minimize the energy cost while ensuring user comfort. A policy network that can output both discrete and continuous actions is designed to generate actions for different types of devices in a smart home. The proposed DRL-agent is trained using a proximal policy optimization approach with historical data and is used for real-time scheduling. Finally, simulations based on real-world data demonstrate the effectiveness and robustness of the proposed algorithm.

Limitations in communication networks, such as delays, packet dropouts, and insufficient bandwidth, create great challenges in the output tracking control and regulation performance of networked control systems. In this article, we propose some novel performance indexes for achieving the optimal tracking and regulation performance. By considering the influences of noise, packet dropouts, and logarithmic quantization on the channel input, the performance limitations of the networked systems are investigated by applying a two-degree-of-freedom (TDOF) control technique. The results obtained prove that the tracking and regulation performance is dependent not only on the position of the plant’s nonminimum phase zeros and unstable poles, but also on the directions. Furthermore, it is demonstrated that noise and quantization error have a negative influence on the control performance, and the design of the TDOF controller can eliminate the effect of packet dropouts on control performance. To validate the innovation results, several simulations are conducted.