Practical Fixed-Time Adaptive ERBFNNs Event-Triggered Control for Uncertain Nonlinear Systems With Dead-Zone Constraint
针对输入死区约束和通信资源受限的不确定非线性系统，提出一种扩展径向基函数神经网络自适应事件触发控制方法，能在固定时间内使跟踪误差收敛到可调小集合，并节省通信资源。
The issue of practical fixed-time control is investigated for a category of uncertain nonlinear systems with input dead-zone constraint. Many practical control systems are subject to the constraint of communication resources and input dead zone, which affects the system’s performance and even results in system instability. To handle the above problems, an extended radial basis function neural networks (ERBFNNs) adaptive event-triggered control method is developed to enable the online compensation of input dead zone and schedule the update of control signals. On this foundation, based on the fixed-time stability theorem, a practical fixed-time event-triggered controller is established by the backstepping technique. Technically, the controller can guarantee that the tracking error converges into a small and adjustable set in a fixed time under different initial states, and the boundary of convergence time is dependent on the adjustable design parameters. Meanwhile, all the closed-loop signals are bounded, the communication resources are saved, and the Zeno behavior is also avoided. Finally, some simulation examples are given to illustrate the validity of the presented strategy.