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Volume 5 Issue 5
Aug.  2018

IEEE/CAA Journal of Automatica Sinica

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Tingting Gao, Yan-Jun Liu, Lei Liu and Dapeng Li, "Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 923-933, Sept. 2018. doi: 10.1109/JAS.2018.7511195
Citation: Tingting Gao, Yan-Jun Liu, Lei Liu and Dapeng Li, "Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 923-933, Sept. 2018. doi: 10.1109/JAS.2018.7511195

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

doi: 10.1109/JAS.2018.7511195
Funds:

the National Natural Science Foundation of China 61622303

the National Natural Science Foundation of China 61603164

the National Natural Science Foundation of China 61773188

the Program for Liaoning Innovative Research Team in University LT2016006

the Fundamental Research Funds for the Universities of Liaoning Province JZL201715402

More Information
  • 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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