IEEE Trans Neural Netw Learn Syst. 2022 Jul;33(7):2892-2902. doi: 10.1109/TNNLS.2020.3046865. Epub 2022 Jul 6.
This article is concerned with an issue of fixed time adaptive neural control for a class of uncertain nonlinear systems subject to hysteresis input and immeasurable states. The state observer and neural networks (NNs) are used to estimate the immeasurable states and approximate the unknown nonlinearities, respectively. On this foundation, an adaptive fixed time neural control strategy is developed. Technically, this control strategy is based on a novel fixed-time stability criterion. Different from the research on fixed-time control in the conventional literature, this article designs a new controller with two fractional exponential powers. In the light of the established stability criterion, the fixed-time stability of the systems is guaranteed under the proposed control scheme. Finally, a simulation study is carried out to test the performance of the developed control strategy.
本文针对一类具有迟滞输入和不可测状态的不确定非线性系统，研究了固定时间自适应神经网络控制问题。状态观测器和神经网络（NNs）分别用于估计不可测状态和逼近未知非线性。在此基础上，提出了一种自适应固定时间神经网络控制策略。从技术上讲，该控制策略基于一种新的固定时间稳定性准则。与传统文献中关于固定时间控制的研究不同，本文设计了一种具有两个分数指数幂的新型控制器。根据所建立的稳定性准则，在所提出的控制方案下保证了系统的固定时间稳定性。最后，进行了仿真研究以测试所开发控制策略的性能。
