Wu Li-Bing, Park Ju H, Xie Xiang-Peng, Liu Ya-Juan
IEEE Trans Neural Netw Learn Syst. 2021 Feb;32(2):695-707. doi: 10.1109/TNNLS.2020.2979174. Epub 2021 Feb 4.
This article is concerned with a neural adaptive tracking control scheme for a class of multiinput and multioutput (MIMO) nonaffine nonlinear systems with event-triggered mechanisms, which include the fixed thresholds, triggering control inputs, and decreasing functions of tracking errors. Unlike the existing results of nonaffine nonlinear controller decoupling, a novel nonlinear multiple control inputs separated design method is proposed based on the mean-value theorem and the Taylor expansion technique. By this way, a weaker condition of nonlinear decoupling is provided to instead of the previous ones. Then, introducing a prescribed performance barrier Lyapunov function (PPBLF) and using neural networks (NNs), the presented event-triggered controller can maintain better tracking performance and effectively alleviate the computation burden of the communication procedure. Furthermore, it is proved that all the closed-loop signals are bounded and the system output tracking errors are confined within the prescribed bounds. Finally, the simulation results are given to demonstrate the validity of the developed control scheme.
本文研究了一类具有事件触发机制的多输入多输出（MIMO）非仿射非线性系统的神经自适应跟踪控制方案，该机制包括固定阈值、触发控制输入以及跟踪误差的递减函数。与现有的非仿射非线性控制器解耦结果不同，基于中值定理和泰勒展开技术，提出了一种新颖的非线性多控制输入分离设计方法。通过这种方式，提供了一个比以前更弱的非线性解耦条件。然后，引入规定性能障碍李雅普诺夫函数（PPBLF）并使用神经网络（NNs），所提出的事件触发控制器可以保持更好的跟踪性能，并有效减轻通信过程的计算负担。此外，证明了所有闭环信号都是有界的，并且系统输出跟踪误差被限制在规定的范围内。最后，给出了仿真结果以证明所提出控制方案的有效性。
