基于事件触发机制的一类非严格反馈非线性系统的自适应神经网络追踪控制

Adaptive neural network tracking control for a class of nonstrict-feedback nonlinear systems based on event-triggering mechanism

基于事件触发机制,研究了一类非严格反馈非线性系统的自适应神经网络追踪控制问题.结合反步技术、神经网络和事件触发机制,提出了一种自适应神经网络控制方案,减少了数据传输量并减轻了控制器和执行器之间的传递负担,保证了输出信号尽可能地追踪到参考信号,同时使得闭环系统的所有信号有界.此外,通过避免芝诺现象保证了所提事件触发机制的可行性.最后,给出一个例子验证了所提出策略的有效性.

In this paper,the adaptive neural network tracking control is addressed based on event-triggering mechanism for a class of nonstrict-feedback nonlinear systems.By combining backstepping technology,neural network and event-triggering mechanism,an adaptive neural network control scheme is proposed,which reduces the data amount transmitted between the controller and the actuator,ensures the output signal to track the reference signal as much as possible,and guarantees all the signals of the closed-loop system to be bounded.In addition,the feasibility of the proposed event-triggering mechanism is ensured by avoiding Zeno phenomenon.Finally,an example is given to verify the effectiveness of the proposed scheme.