Quadrotor UAVs, renowned for their agility and versatility, are extensively utilized in a range areas. However, their inherent underactuated dynamic characteristics render them particularly vulnerable to external disturbances and systemic failures. To address this issue, our study introduces a hybrid control method tailored to combat the most prevalent types of drone failures—actuator faults. This innovative approach leverages reinforcement learning to enhance fault tolerance. Specifically, we employ reinforcement learning techniques to output compensatory control signals that bolster the core functionalities of the base controller. This integration aims to preserve the stability and continuity of mission-critical tasks even in the face of operational faults, thereby ensuring robust safety controls. We utilized the proximal policy optimization algorithm for the strategic training of our control systems. We test in both simulated environments and real-world scenarios was conducted to evaluate the efficacy of our control strategy under conditions of actuator failure. The results affirm that our method significantly enhances the safety and stability of drone operations, maintaining control integrity during rotor failures.

Owing to its unique, concealment and easy customization by combining different wrist and hand gestures, high-density surface electromyogram (HD-sEMG) is recognized as a potential solution to the next generation biometric authentication, which usually adopts a wireless body sensor network (BSN) to acquire the multi-channel HD-sEMG biosignals from distributed electrode arrays. For more accurate and reliable classification, biometric authentication requires the distributed biosignals to be sampled simultaneously and be well aligned, which means that the sampling jitters among the arrays need to be tiny. To synchronize data sampling clocks of a cluster of BSN nodes for biometric authentication, this article modifies the packet-coupled oscillators protocol by using a dynamic controller (D-PkCOs). This protocol only involves one-way single packet exchange, which reduces the communication overhead significantly. For the purpose of maintaining precise sampling of these BSN nodes subject to drifting clock frequency and varying delays, the dynamic controller is designed via the $H_\infty$ robust method, and it is proved that all the BSN nodes’ sampling jitters are bounded. The experimental results demonstrate that the D-PkCOs protocol can keep the sampling jitters less than a microsecond in a 10-node IEEE 802.15.4 network. The application of D-PkCOs to the BSN shows that the HD-sEMG signal with a high signal-to-noise ratio is obtained, which leads to better gesture classification performance.

This technical paper investigates the cluster synchronization of finite-field networks (FFNs) based on the algebraic state space representation. By resorting to the semi-tensor product of matrices, the cluster synchronization of an FFN can be completely converted into the set stability of state trajectory. Then, a necessary and sufficient condition for the cluster synchronization of FFNs is obtained based on the invariant subset and one-step transition matrix. In particular, the obtained results are applicable to check the leader–follower synchronization and group consensus of FFNs. Finally, a numerical example is given to illustrate the feasibility of the obtained results.

Multiagent formation obstacle avoidance is a crucial research topic in the field of multiagent cooperative control, and deep reinforcement learning has shown remarkable potential in this domain. However, most existing studies are not fully distributed and often involve relatively simple scenarios. In this article, we propose an advanced method based on multiagent deep reinforcement learning to address formation and obstacle avoidance in dynamic obstacles environments. For handling complex environments with an unknown number of obstacles, we use long short-term memory (LSTM) networks to encode dynamic obstacles, thereby improving the efficiency of obstacle avoidance. Our method achieves formation and obstacle avoidance in scenarios with both dynamic and static obstacles, where agents coordinate through fully independent and autonomous decision-making. We utilize the multiagent proximal policy optimization (MAPPO) algorithm for centralized training and distributed execution, enhancing the agents' formation and obstacle avoidance capabilities in complex settings. Through simulation and real-world experiments, and by comparing with benchmark methods, we demonstrate significant improvements in formation effectiveness and obstacle avoidance success rates, showcasing the superiority and practicality of our proposed approach.