期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 3, 页码 683-694出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2523878
关键词
Adaptive dynamic programming (ADP); eventtrigger; neural network; observer; online learning and control
类别
资金
- National Science Foundation [ECCS 1053717, IIS 1526835]
- Army Research Office [W911NF-12-1-0378]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1053717] Funding Source: National Science Foundation
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [GRANTS:13729795, 1526835] Funding Source: National Science Foundation
This paper proposes a novel event- triggered adaptive dynamic programming (ADP) control method for nonlinear continuous-time system with unknown internal states. Comparing with the traditional ADP design with a fixed sample period, the event- triggered method samples the state and updates the controller only when it is necessary. Therefore, the computation cost and transmission load are reduced. Usually, the event-triggered method is based on the system entire state which is either infeasible or very difficult to obtain in practice applications. This paper integrates a neural-network-based observer to recover the system internal states from the measurable feedback. Both the proposed observer and the controller are aperiodically updated according to the designed triggering condition. Neural network techniques are applied to estimate the performance index and help calculate the control action. The stability analysis of the proposed method is also demonstrated by Lyapunov construct for both the continuous and jump dynamics. The simulation results verify the theoretical analysis and justify the efficiency of the proposed method.
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