Event-Triggered Adaptive Optimal Control With Output Feedback: An Adaptive Dynamic Programming Approach

This article presents an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. First, it is shown that the unmeasurable states can be reconstructed by using the measured input and output data. An event-based feedback strategy is then proposed to reduce t...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-11, Vol.32 (11), p.5208-5221
Main Authors: Zhao, Fuyu, Gao, Weinan, Jiang, Zhong-Ping, Liu, Tengfei
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive dynamic programming (ADP)
Adaptive systems
Algorithms
Control methods
Dynamic programming
event-triggered control
Feedback
Iterative methods
Linear systems
Optimal control
Output feedback
Riccati equation
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Summary:This article presents an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. First, it is shown that the unmeasurable states can be reconstructed by using the measured input and output data. An event-based feedback strategy is then proposed to reduce the number of controller updates and save communication resources. The discrete-time algebraic Riccati equation is iteratively solved through event-triggered adaptive dynamic programming based on both policy iteration (PI) and value iteration (VI) methods. The convergence of the proposed algorithm and the closed-loop stability is carried out by using the Lyapunov techniques. Two numerical examples are employed to verify the effectiveness of the design methodology.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3027301