Department of Mechanical Engineering, Politecnico di Milano, Milano 20156, Italy.
Harbin Institute of Technology, Harbin 150001, China.
Neural Netw. 2021 Nov;143:377-385. doi: 10.1016/j.neunet.2021.06.019. Epub 2021 Jun 23.
The problem of event-triggered neural adaptive fault-tolerant finite-time control is investigated for a class of nonstrict feedback nonlinear systems in the presence of nonaffine nonlinear faults. The event-triggered signal is designed by using a relative-threshold to reduce communication burden. The dynamic surface control method is used to relax the assumption of the reference signal and deal with the computational complexity issue. Based on the finite-time stability, a new neural adaptive backstepping design method is developed. The event-triggered neural adaptive fault-tolerant control law is developed for the closed-loop system so that not only the semi-global practical finite-time stability is ensured, but also the tracking performance with a small residual set is guaranteed. Finally, the effectiveness of the proposed control law is verified via simulation results.
针对一类存在非仿射非线性故障的非严格反馈非线性系统，研究了事件触发神经自适应容错有限时间控制问题。通过使用相对阈值设计事件触发信号，以降低通信负担。采用动态面控制方法放宽参考信号的假设，并处理计算复杂度问题。基于有限时间稳定性，提出了一种新的神经自适应反推设计方法。为闭环系统设计了事件触发神经自适应容错控制律，不仅保证了半全局实用有限时间稳定性，还保证了具有小残留集的跟踪性能。最后，通过仿真结果验证了所提出控制律的有效性。
