Adaptive-critic-based event-driven nonlinear robust state feedback
Ding Wang, Chaoxu Mu, Haibo He, Derong Liu · 2016
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.