Adaptive neural network control for nonlinear state constrained systems with unknown dead-zones input

Wei Zhao; Lei Liu; Yan-Jun Liu; Wei Zhao; Lei Liu; Yan-Jun Liu

doi:10.3934/math.2020261

AIMS Mathematics

2020, Volume 5, Issue 5: 4065-4084. doi: 10.3934/math.2020261

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Research article

Adaptive neural network control for nonlinear state constrained systems with unknown dead-zones input

1.
College of Electrical Engineering, Liaoning University of Technology-121001 Jinzhou, China
2.
College of Science, Liaoning University of Technology-121001 Jinzhou, China

Received: 11 January 2020 Accepted: 27 March 2020 Published: 27 April 2020
MSC : 93B52, 93C95, 93D05

In this paper, an adaptive neural network tracking control problem for a class of strict feedback systems is disposed. The neural network adaptive control method is introduced in this paper to simplify the controller design. The difficulty in this article is the constraint problem and how to resolve dead-zones in the system. In order to overcome these difficulties, the Barrier Lyapunov functions (BLF) and backstepping process are introduced to ensure that the full state constraint is implemented, meanwhile, keep the system output as close as possible to trace the desired trajectory. Dead-zone compensation method is also plays an important role in controller design. Delay constraint is introduced to solve the problem of uncertain initial state. In the end, the stability of the closed-loop system is proved. Simulation results show that the developed method is effective.
- adaptive control,
- dead-zone,
- barrier Lyapunov functions,
- neural network
Citation: Wei Zhao, Lei Liu, Yan-Jun Liu. Adaptive neural network control for nonlinear state constrained systems with unknown dead-zones input[J]. AIMS Mathematics, 2020, 5(5): 4065-4084. doi: 10.3934/math.2020261

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Abstract

In this paper, an adaptive neural network tracking control problem for a class of strict feedback systems is disposed. The neural network adaptive control method is introduced in this paper to simplify the controller design. The difficulty in this article is the constraint problem and how to resolve dead-zones in the system. In order to overcome these difficulties, the Barrier Lyapunov functions (BLF) and backstepping process are introduced to ensure that the full state constraint is implemented, meanwhile, keep the system output as close as possible to trace the desired trajectory. Dead-zone compensation method is also plays an important role in controller design. Delay constraint is introduced to solve the problem of uncertain initial state. In the end, the stability of the closed-loop system is proved. Simulation results show that the developed method is effective.

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