IEEE Trans Cybern. 2020 Jul;50(7):2946-2958. doi: 10.1109/TCYB.2019.2921733. Epub 2019 Jul 18.
This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.
本文针对一类严格反馈形式的离散时间非线性系统，提出了一种新的事件触发（ET）自适应神经网络控制方案。在所提出的方案中，理想控制输入是在递归设计过程中导出的，该过程仅依赖于系统状态，与虚拟控制律无关。在这种情况下，使用高阶神经网络（NN）来逼近理想控制输入（而不是虚拟控制律），然后在 ET 机制下开发相应的自适应神经网络控制器。设计了一种改进的 NN 权值更新律，在触发时刻非周期性地调整，以保证所有采样时刻 NN 权值估计的一致最终有界性（UUB）。利用有界的 NN 权值估计值和一个死区算子，构造 ET 条件和自适应 ET 阈值系数，通过 Lyapunov 稳定性理论保证闭环网络控制系统的 UUB，从而大大减轻了网络通信负载。所提出的 ET 条件易于实现，因为避免了：1）在回溯过程中使用中间 ET 条件；2）计算虚拟控制律；3）当系统状态收敛到期望区域时，事件的冗余触发。通过仿真结果验证了所提出方案的有效性。
