The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Neural Netw. 2018 Jun;102:27-35. doi: 10.1016/j.neunet.2018.02.007. Epub 2018 Feb 21.
The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties.
通过事件触发机制和自适应评论家学习技术研究了具有不确定项的连续时间非线性系统的鲁棒控制综合。我们主要关注将事件触发机制与自适应评论家设计相结合，以解决非线性鲁棒控制问题。这不仅可以更好地利用计算和通信资源，还可以从智能优化的角度进行控制器设计。通过理论分析，通过获得具有新定义成本函数和一定触发条件的标称系统的事件触发最优控制律，可以实现非线性鲁棒稳定。自适应评论家技术被用来方便事件触发控制设计，其中神经网络被引入作为学习阶段的逼近器。通过仿真研究和比较验证了事件触发鲁棒控制方案的性能。本方法将事件触发控制和自适应评论家控制的应用领域扩展到具有动态不确定性的非线性系统。
