Abstract
<abstract><p>The fault-tolerant control problem of a hydraulic servo actuator in the presence of actuator faults is studied utilizing adaptive dynamic programming. This task is challenging because of unknown system dynamics, uncertain disturbances or unmeasurable system states of such highly nonlinear systems in real applications. The aim is to achieve asymptotic tracking and actuator faults compensation by minimizing some predefined performance index. The discrete-time algebraic Riccati equation is iteratively solved by the adaptive dynamic programming approach. For practical reasons, adaptive dynamic programming techniques and fault compensation are integrated to iteratively compute an approximated optimal fault-tolerant control using real-time input/output data without any a priori knowledge of the system dynamics and unmeasurable states. As a result, a fault-tolerant control of hydraulic servo actuator is then designed based on adaptive dynamic programming via output feedback. Also, the convergence analysis of a data-driven fault-tolerant control is theoretically shown as well. Finally, intensive simulation results are given to prove the validity and merits of the developed data-driven fault-tolerant control strategy.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Reference47 articles.
1. P. Werbos, Handbook of intelligent control: Neural, fuzzy, and adaptive approaches, In: White DA, Sofge DA, editors. Approximate dynamic programming for real-time control and neural modeling, New York: Van Nostrand Reinhold, 1992,493–525.
2. Q. Wei, D. Liu, H. Lin, Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems, IEEE Trans. Cybern., 46 (2016), 840–853. https://doi.org/10.1109/TCYB.2015.2492242
3. X. Yang, D. Liu, Q. Wei, D. Wang, Guaranteed cost neural tracking control for a class of uncertain nonlinear systems using adaptive dynamic programming, Neurocomputing, 198 (2016), 80–90. https://doi.org/10.1016/j.neucom.2015.08.119
4. L. Dong, X. Zhong, C. Sun, H. He, Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems, IEEE Trans. Neural Networks Learn. Syst., 28 (2016), 1594–1605. https://doi.org/10.1109/TNNLS.2016.2541020
5. Q. Sun, C. Zhang, N. Jiang, J. Yu, L. Xu, Data-driven nonlinear near-optimal regulation based on multi-dimensional Taylor network dynamic programming, IEEE Access, 8 (2020), 36476–36484. https://doi.org/10.1109/ACCESS.2020.2975391
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