Event-triggered control is an effective control strategy, capable of reducing the amount of communications and retaining a satisfactory closed-loop performance. In this paper, we aim at proposing an event-triggered sampling mechanism and studying observer-based output feedback control for switched linear neutral systems with mixed time-varying delays. Different from the conventional event-triggered control strategies, our proposed one transmits not only the state but also the switching information to the controller, which is advantageous in applications where the measured outputs and the switching information have to be transmitted over a communication network. Moreover, asynchronous switchings may be caused between the subsystems and their matched sub-controllers under the proposed sampling mechanism, which increases difficulties in analyzing stability. We develop a sufficient condition, under which the proposed control scheme guarantees globally exponential stability of the closed-loop system meanwhile taking into account asynchronous switchings. Finally, an illustrative example is given to show the effectiveness of the proposed method.

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this article presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decision-making. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks.

This paper presents the design, implementation, and characterization of a hardware platform applicable to a self-powered wireless sensor network (WSN) node. Its primary design objective is to devise a hybrid energy harvesting system to extend the operational lifetime of WSN node after they are deployed in the field environment. Besides the implementation of optimal components (microcontroller, sensor, radio frequency transceiver, and others) to achieve the lowest power consumption, it is also necessary to consider the sources of energy instead of the frequent recharging or replacement of batteries. Therefore, the platform incorporates a multisource energy harvesting module to collect energy from the surrounding environment, including wind, solar radiation, and thermal energy. The platform also includes an energy storage module through a super-capacitor, RF transceiver module, and the primary microcontroller module. Experimental results showed that the WSN node system with appropriate integration will reserve sufficient energy and meet the long-term power supply requirements of the WSN node without batteries in the field environment. The experimental results and empirical measurements taken over nine days demonstrated that the average daily generating capacity was 7805.09 J, which is far more than the energy consumption of the WSN node (about 2972.88 J).

Naturally, one wonders when and how the Attacker can chase the Target and when and how the Attacker can evade the Defender. In this paper, we first analyze the possibility of winning the game for the Attacker under the framework of games of kind. Then, we present three different stages and corresponding control strategies, as well as the conditions for switching stages. In addition, the Target cooperates with the Defender to compose a team as the opponent of the Attacker. Different capture radii also affect cooperation between the Target and Defender. When the capture radius of the Defender is less than that of the Attacker, the Target and Defender cooperate to avoid being captured by the Attacker or to extend the time to be captured. Conversely, when the capture radius of the Attacker is less than that of the Defender, the Target-Defender team can adopt a rendezvous strategy to end the engagement. We derive the initial state under which the Target can rendezvous with the Defender before the Target is captured by the Attacker.