Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 33, Issue 12, Pages 7513-7522Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3085324
Keywords
Adaptive systems; Control design; Nonlinear systems; Control systems; Stability criteria; Backstepping; Lyapunov methods; Adaptive neural control; barrier Lyapunov function (BLF); full state constraints; nonlinear systems; prescribed finite time
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Funding
- National Natural Science Foundation of China [61873137]
- Shandong Taishan Scholar Project [ts20190930]
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This article presents a novel backstepping-based adaptive neural tracking control design procedure for nonlinear systems with time-varying state constraints. The designed controller ensures that the output tracking error converges to a small neighborhood of the origin with the prescribed finite time and accuracy level. The proposed control scheme is further validated through simulation examples.
The purpose of this article is to present a novel backstepping-based adaptive neural tracking control design procedure for nonlinear systems with time-varying state constraints. The designed adaptive neural tracking controller is expected to have the following characters: under its action: 1) the designed virtual control signals meet the constraints on the corresponding virtual control states in order to realize the backstepping design ideal and 2) the output tracking error tends to a sufficiently small neighborhood of the origin with the prescribed finite time and accuracy level. By combining the barrier Lyapunov function approach with the adaptive neural backstepping technique, a novel adaptive neural tracking controller is proposed. It is shown that the constructed controller makes sure that the output tracking error converges to a small neighborhood of the origin with the prespecified tracking accuracy and settling time. Finally, the proposed control scheme is further tested by simulation examples.
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