Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 28, Issue 7, Pages 1594-1605Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2541020
Keywords
Event-triggered control; heuristic dynamic programming (HDP); nonlinear discrete-time systems
Categories
Funding
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- U.S. National Science Foundation within the Division of Information and Intelligent Systems [1526835]
- U.S. National Science Foundation within the Division of Electrical, Communications and Cyber Systems [1053717]
- National Natural Science Foundation of China [61533008, 61520106009, 51529701]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1526835] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1053717] Funding Source: National Science Foundation
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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.
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