Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 8, Pages 2942-2954Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2934403
Keywords
Artificial neural networks; Lyapunov methods; Adaptive control; Nonlinear systems; Learning systems; Stability analysis; Adaptive control; barrier function (BF); global stability; neural networks (NNs); strict-feedback systems
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Funding
- National Natural Science Foundation of China [61733006, U1611262, 61803097]
- China National Funds for Distinguished Young Scientists [61425009]
- Innovative Research Team Program of Guangdong Province Science Foundation [2018B030312006]
- China Postdoctoral Science Foundation [2018M640764]
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In this article, a globally stable adaptive control strategy for uncertain strict-feedback systems is proposed within predefined neural network (NN) approximation sets, despite the presence of unknown system nonlinearities. In contrast to the conventional adaptive NN control results in the literature, a primary benefit of the developed approach is that the barrier Lyapunov function is employed to predefine the compact set for maintaining the validity of NN approximation at each step, thus accomplishing the global boundedness of all the closed-loop signals. Simulation results are performed to clarify the effectiveness of the proposed methodology.
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