Event-Driven Adaptive Robust Control of Nonlinear Systems With Uncertainties Through NDP Strategy
针对连续时间不确定非线性系统，通过系统变换和事件驱动最优控制器设计实现鲁棒性，并利用神经动态规划技术完成控制器设计，仿真验证了方法的有效性。
In this paper, we construct an event-driven adaptive robust control approach for continuous-time uncertain nonlinear systems through a neural dynamic programming (NDP) strategy. Through system transformation and theoretical analysis, the robustness of the original uncertain system can be achieved by designing an event-driven optimal controller with respect to the nominal system under a suitable triggering condition. In addition, it is also observed that the event-driven controller has a certain degree of gain margin. Then, the NDP technique is employed to perform the main controller design task, followed by the uniform ultimate boundedness stability proof with the feedback action of the event-driven adaptive control law. The comparative effect of the present control strategy is also illustrated via two simulation examples. The established method provides a new avenue of combining adaptive dynamic programming-based self-learning control, event-triggered adaptive control, and robust control, to investigate the nonlinear adaptive robust feedback design under uncertain environment.