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

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 11; pp. 5208 - 5221
• Main Authors: Zhao, Fuyu, Gao, Weinan, Jiang, Zhong-Ping, Liu, Tengfei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3027301

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.