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Policy Iteration and Event-Triggered Robust Adaptive Dynamic Programming for Large-Scale Systems⁎This work was supported in part by the National Natural Science Foundation of China under Grants 61633007, 61533007 and U1911401, and in part by the U.S. National Science Foundation under Grant EPCN-1903781. Issue 14 (2021)
Record Type:
Journal Article
Title:
Policy Iteration and Event-Triggered Robust Adaptive Dynamic Programming for Large-Scale Systems⁎This work was supported in part by the National Natural Science Foundation of China under Grants 61633007, 61533007 and U1911401, and in part by the U.S. National Science Foundation under Grant EPCN-1903781. Issue 14 (2021)
Main Title:
Policy Iteration and Event-Triggered Robust Adaptive Dynamic Programming for Large-Scale Systems⁎This work was supported in part by the National Natural Science Foundation of China under Grants 61633007, 61533007 and U1911401, and in part by the U.S. National Science Foundation under Grant EPCN-1903781.
Abstract: In this paper, an event-triggered robust optimal control approach is proposed for large-scale systems with both parametric and dynamic uncertainties through robust adaptive dynamic programming, policy iteration, and small-gain techniques. By using the input and output data, the unmeasurable states are reconstructed instead of constructing a Luenberger observer. Starting from an admissible control policy, an event-based feedback control policy is learned to save the communication resources and reduce the number of control updates. The closed-loop stability and the convergence of the proposed algorithm are analyzed by using Lyapunov and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed method.