期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 1, 页码 77-86出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.2977318
关键词
Control design; Cost function; Optimal control; Nonlinear systems; Adaptive systems; Switches; Adaptive learning system; discount factor; guaranteed cost function; neuro-optimal control; uncertainty
类别
资金
- National Natural Science Foundation of China [61773373, 61873268, U1913209, 61890930-5]
- Beijing Natural Science Foundation [JQ19013, JQ19020]
- National Key Research and Development Project [2018YFC1900800-5]
This article investigates adaptive optimal feedback stabilization for discounted guaranteed cost control of uncertain nonlinear dynamical systems. The problem is transformed to design a discounted optimal control policy for the nominal plant, and an improved self-learning algorithm under the framework of adaptive critic designs is established for deriving the approximate optimal solution. The effectiveness of the method is illustrated through simulation verification on the F16 aircraft plant and other dynamics.
The adaptive optimal feedback stabilization is investigated in this article for discounted guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis, the guaranteed cost control problem involving a discounted utility is transformed to the design of a discounted optimal control policy for the nominal plant. The size of the neighborhood with respect to uniformly ultimately bounded stability is discussed. Then, for deriving the approximate optimal solution of the modified Hamilton-Jacobi-Bellman equation, an improved self-learning algorithm under the framework of adaptive critic designs is established. It facilitates the neuro-optimal control implementation without an additional requirement of the initial admissible condition. The simulation verification toward several dynamics is provided, involving the F16 aircraft plant, in order to illustrate the effectiveness of the discounted guaranteed cost control method.
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