Wang Jianhui, Gong Qijuan, Huang Kunfeng, Liu Zhi, Chen C L Philip, Liu Jie
IEEE Trans Neural Netw Learn Syst. 2023 Sep;34(9):5590-5600. doi: 10.1109/TNNLS.2021.3129816. Epub 2023 Sep 1.
For a class of uncertain nonlinear systems with actuator failures, the event-triggered prescribed settling time consensus adaptive compensation control method is proposed. The unknown form of actuator failures may occur in practical applications, resulting in system instability or even control failure. In order to effectively deal with the above problems, a neural network adaptive control method is developed to ensure that the system states rapidly converge in the event of failure and compensate for the failures of actuator. Meanwhile, a nonlinear transformation function is introduced to make sure that the tracking error converges for the predefined interval within a prescribed settling time, which makes that the convergence time can be preset. Furthermore, a finite-time event-triggered compensation control strategy is established by the backstepping technology. Under this strategy, the system not only can rapidly stabilize in finite time but also can effectively save network bandwidth. In addition, the states of the system are globally uniformly bounded. Finally, the theoretical analysis and simulation experiments validate the effectiveness of the proposed method.
针对一类具有执行器故障的不确定非线性系统，提出了事件触发的规定收敛时间一致性自适应补偿控制方法。在实际应用中可能会出现未知形式的执行器故障，导致系统不稳定甚至控制失效。为了有效解决上述问题，开发了一种神经网络自适应控制方法，以确保系统状态在发生故障时迅速收敛，并补偿执行器的故障。同时，引入了一个非线性变换函数，以确保跟踪误差在规定的收敛时间内在预定义的区间内收敛，这使得收敛时间可以预先设定。此外，通过反步法建立了有限时间事件触发补偿控制策略。在该策略下，系统不仅能在有限时间内迅速稳定，还能有效节省网络带宽。此外，系统状态全局一致有界。最后，理论分析和仿真实验验证了所提方法的有效性。
