Abstract
In order to solve the problem of optimal prescribed performance control for unknown dynamic nonlinear systems, an adaptive dynamic programming method based on dynamic event-triggered control strategy is designed. By using Lyapunov stability theory, it is proved that all signals in nonlinear systems are uniformly and ultimately bounded. First, the system under consideration is transformed into an unconstrained system with the prescribed performance by the variable transformation method. Then, the integral reinforcement learning method is used to solve the optimal control problem when the system drift dynamic is unknown. In addition, a dynamic event-triggered control strategy is constructed, which can update the weight estimation and control strategy irregularly, so as to alleviate the problem of excessive data transmission burden when the designed critic neural network approximates the value function. At the same time, Zeno’s behavior in the communication process is avoided. Finally, a numerical example is given to verify the validity of the proposed theory.





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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grants 62103005, 62173001,62273006. The Natural Science Foundation for Distinguished Young Scholars of Higher Education Institutions of Anhui Province under grant 2022AH020034, the Natural Science Foundation for Excellent Young Scholars of Higher Education Institutions of Anhui Province under grant 2023AH030030,2022AH030049, the research and development project of Engineering Research Center of Biofilm Water Purification and Utilization Technology of Ministry of Education under Grant BWPU2023ZY02, the University Synergy Innovation Program of Anhui Province under Grant GXXT-2023-020.
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Qi, Y., Su, L. Dynamic Event-Triggered Prescribed Performance Control for Partially Unknown Nonlinear System via Adaptive Dynamic Programming. Int. J. Fuzzy Syst. 26, 1651–1663 (2024). https://doi.org/10.1007/s40815-024-01694-3
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DOI: https://doi.org/10.1007/s40815-024-01694-3