Adaptive-critic-based event-driven nonlinear robust state feedback
Date of Original Version
In this paper, we study the adaptive-critic-based event-driven robust state feedback stabilization for a class of uncertain nonlinear systems. The novel idea lies in bringing adaptive dynamic programming, a self-learning optimization approach, into nonlinear robust control area under uncertain environment and event-triggering framework. Through theoretical analysis, the nonlinear robust stabilization is achieved by deriving an event-driven optimal controller of the nominal system. The adaptive-critic-based technique is adopted to facilitate the optimal control design, with a critic neural network being constructed to serve as the learning approximator. The control performance is also verified via simulation study. Significantly, combining the adaptive-critic-based design method with event-triggering formulation is a potential and promising direction of intelligent control since it can make better use of advanced learning behavior and limited computation resources.
2016 IEEE 55th Conference on Decision and Control, CDC 2016
Wang, Ding, Chaoxu Mu, Haibo He, and Derong Liu. "Adaptive-critic-based event-driven nonlinear robust state feedback." 2016 IEEE 55th Conference on Decision and Control, CDC 2016 , (2016): 5813-5818. doi:10.1109/CDC.2016.7799163.