Liu Xinglan, Xu Bin, Cheng Yixin, Wang Hai, Chen Weisheng
IEEE Trans Cybern. 2023 Apr;53(4):2391-2401. doi: 10.1109/TCYB.2021.3119780. Epub 2023 Mar 16.
This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.
本文专注于基于事件学习方案的严格反馈不确定非线性系统的自适应跟踪控制。提出了一种新颖的神经网络（NN）学习律来设计自适应控制方案。基于预测误差的控制过程驱动的NN权重信息在事件触发的情况下间歇地传输到主要用于信号跟踪的NN学习律。由跟踪误差驱动的NN的在线存储采样数据在事件环境中用于更新学习律。通过事件触发通信更新自适应控制和NN学习律，保证了NN学习能力、跟踪性能的提高以及系统计算资源的节省。此外，证明了两种类型事件触发误差的最小时间间隔是有界的，并且严格排除了芝诺行为。最后，仿真结果说明了所提出控制方法的有效性和良好性能。
