Xu Bin, Wang Xia, Shou Yingxin, Shi Peng, Shi Zhongke
IEEE Trans Neural Netw Learn Syst. 2022 Nov;33(11):6173-6182. doi: 10.1109/TNNLS.2021.3072552. Epub 2022 Oct 27.
The tracking control is investigated for a class of uncertain strict-feedback systems with robust design and learning systems. Using the switching mechanism, the states will be driven back by the robust design when they run out of the region of adaptive control. The adaptive design is working to achieve precise adaptation and higher tracking precision in the neural working domain, while the finite-time robust design is developed to make the system stable outside. To achieve good tracking performance, the novel prediction error-based adaptive law is constructed by considering the estimation performance. Furthermore, the output constraint is achieved by imbedding the barrier Lyapunov function-based design. The finite-time convergence and the uniformly ultimate boundedness of the system signal can be guaranteed. Simulation studies show that the proposed approach presents robustness and adaptation to system uncertainty.
针对一类具有鲁棒设计和学习系统的不确定严格反馈系统，研究了其跟踪控制问题。利用切换机制，当状态超出自适应控制区域时，鲁棒设计将使状态被驱动回来。自适应设计致力于在神经工作域内实现精确自适应和更高的跟踪精度，而有限时间鲁棒设计则用于使系统在外部保持稳定。为了实现良好的跟踪性能，通过考虑估计性能构建了基于新颖预测误差的自适应律。此外，通过嵌入基于障碍Lyapunov函数的设计来实现输出约束。可以保证系统信号的有限时间收敛和一致最终有界性。仿真研究表明，所提出的方法对系统不确定性具有鲁棒性和适应性。
