Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems with Control Constraints
Date of Original Version
In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton-Jacobi-Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method.
IEEE Transactions on Neural Networks and Learning Systems
Dong, Lu, Xiangnan Zhong, Changyin Sun, and Haibo He. "Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems with Control Constraints." IEEE Transactions on Neural Networks and Learning Systems 28, 8 (2017): 1941-1952. doi:10.1109/TNNLS.2016.2586303.