Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints |
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Authors: | Tingting Gao Yan-Jun Liu Lei Liu Dapeng Li |
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Affiliation: | 1.College of Science, Liaoning University of Technology, Jinzhou 121000, China2.School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China |
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Abstract: | In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. |
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Keywords: | Adaptive control neural networks (NNs) nonlinear pure-feedback systems time-varying constraints |
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