Cheng Jun, Wu Junhui, Yan Huaicheng, Zhang Dan, Wu Zheng-Guang, Zhai Ying
IEEE Trans Cybern. 2025 Jul;55(7):3276-3284. doi: 10.1109/TCYB.2025.3569806.
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.
本研究探讨了未知死区情况下切换信息物理系统的自适应神经网络异步控制问题。采用广义切换规则而非马尔可夫/半马尔可夫过程来研究子系统的切换行为。该方法利用基于单模的驻留时间来刻画驻留概率的动态特性，旨在在满足实际场景需求的同时降低计算负担。考虑到网络环境的复杂性，研究了未知死区输入，可通过基于自适应神经网络的控制律有效实现。为了抵消意外信息的不利影响，开发了一种基于饱和的观测器，其中饱和水平可动态调整，以期提供更大的灵活性。利用与检测模式和系统模式相关的李雅普诺夫函数，建立了充分准则以确保闭环系统在概率上保持有界。最终，通过两个仿真例子验证了所提出控制方法的实用性和有效性。
