Adaptive Neural Network-Based Asynchronous Control for Switching Cyber–Physical Systems With Unknown Dead Zone
研究了切换信息物理系统在未知死区下的自适应神经网络异步控制问题，提出一种基于广义切换规则和饱和观测器的控制方法，确保系统概率有界，并通过仿真验证了有效性。
This study investigates the problem of adaptive neural network asynchronous control for switching cyber-physical systems under unknown dead zones. A generalized switching rule, instead of a Markov/semi-Markov process, is utilized to scrutinize the switching behavior of subsystems. This approach characterizes the dynamic nature of sojourn probabilities using single-mode-based sojourn time, aiming to decrease computational load while meeting the demands of real-world scenarios. Considering the intricacies of network environments, the unknown dead zone inputs are considered, which can be effectively implemented via the adaptive neural network-based control law. To counteract the adverse effects of unforeseen information, a saturation-based observer is developed, in which the saturation level is dynamically adjusted with the hope of providing greater flexibility. Utilizing a Lyapunov function that correlates with the detected mode and the system mode, sufficient criteria are established to ensure that the closed-loop system remains bounded in probability. Eventually, the practicality and effectiveness of the proposed control methodology are verified through two simulated examples.