Adaptive dynamic surface neural network control for nonstrict-feedback uncertain nonlinear systems with constraints |
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Authors: | Junkang Ni Ling Liu Wei He Chongxin Liu |
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Institution: | 1.The Department of Electrical Engineering, School of Automation,Northwestern Polytechnical University,Xi’an,China;2.The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering,Xi’an Jiaotong University,Xi’an,China;3.The School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing,China |
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Abstract: | This paper presents an adaptive dynamic surface neural network control for a class of nonstrict-feedback uncertain nonlinear systems subjected to input saturation, dead zone and output constraint. The problem of input saturation is solved by designing an anti-windup compensator, and the issue of output constraint is addressed by introducing tan-type Barrier Lyapunov function. Furthermore, based on adaptive backstepping technique, a series of novel stabilizing functions are derived. First-order sliding mode differentiator is introduced into backstepping design to obtain the first-order derivative of virtual control. The real control input is obtained using dead-zone inverse method. It is proved that the proposed control scheme can achieve finite time convergence of the output tracking error into a small neighbor of the origin and guarantee all the closed-loop signals are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme. |
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