Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy

Chunlei Zhang,Qiang Qu,Lidong Wang,Xuebo Chen,Chuang Gao

doi:10.1109/access.2020.2984344

Chunlei Zhang, Qiang Qu + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.2984344

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: University of Science and Technology Liaoning

Abstract

In this paper, an adaptive event-triggered tracking control problem is considered for a class of pure-feedback nonlinear systems with output constraints. The mean value theorem is used to transform the pure-feedback system in non-affine form into a system in affine form. In addition, the radial basis function neural network (RBF NN) control is used to approximate the unknown nonlinear function in the system and the tracking error of the controller is limited to a small constant boundary by using the positive obstacle Lyapunov function. An adaptive controller for a class of pure-feedback systems is established, which based on the backstepping control theory and event-triggered control theory, it can ensure all the closed-loop signals are bounded and avoid the Zeno-behavior. The simulation results prove the effectiveness of the controller design.

Highlights

In recent years, the research of nonlinear system control has become the focus of scholars
The unknown nonlinear functions of the system are often approximated by fuzzy logic systems (FLSs) and neural networks (NNs) control
The contributions of this paper are described as below: (1) For the first time, both output constraints and eventtriggered strategies are considered in the pure-feedback nonlinear system control problem, the controller can ensure that all the closed-loop signals are semi-globally bounded, the tracking error of the system can be reduced to a small boundary by the positive obstacle Lyapunov function and there is a time interval to ensure avoid the Zneo-behavior

Summary

INTRODUCTION

The research of nonlinear system control has become the focus of scholars. Reference [12] considered an adaptive neural control for pure-feedback nonlinear systems with event-triggered communications. Reference [22] considered an adaptive fuzzy controller for non-triangular structural stochastic switched nonlinear systems with full state constraints, using barrier Lyapunov function to constrain the state of the system. Reference [33] considered an event-triggered adaptive backstepping control design for parametric strict-feedback nonlinear systems. This paper designs an adaptive event-triggered neural network controller for a pure feedback system with output constraints. (1) For the first time, both output constraints and eventtriggered strategies are considered in the pure-feedback nonlinear system control problem, the controller can ensure that all the closed-loop signals are semi-globally bounded, the tracking error of the system can be reduced to a small boundary by the positive obstacle Lyapunov function and there is a time interval to ensure avoid the Zneo-behavior.

BACKGROUND

RBF NEURAL NETWORK

CONTROL DESIGN AND STABILITY ANALYSIS

SIMULATION

EXPERIMENTAL SIMULATION

CONCLUSION