Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems
Date of Original Version
This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.
IEEE Transactions on Neural Networks and Learning Systems
Dong, Lu, Xiangnan Zhong, Changyin Sun, and Haibo He. "Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems." IEEE Transactions on Neural Networks and Learning Systems 28, 7 (2017): 1594-1605. doi:10.1109/TNNLS.2016.2541020.