Robust adaptive tracking control for unmanned helicopter with constraints

Rong Li,Jianye Liu,Qingxian Wu,Mou Chen

doi:10.1177/1729881417712621

Abstract

In this article, a prescribed performance-based adaptive neural network control scheme is proposed for an uncertain small-scale unmanned helicopter system subject to input saturations and output constraints. The radial basis function neural networks are employed to approximate system uncertainties. A nonlinear disturbance observer is developed to tackle input saturation. Meanwhile, the prescribed performance function is adopted to deal with output constraint. The closed-loop system stability is rigorously proved using Lyapunov synthesis. Finally, simulation results for unmanned helicopter system are presented to demonstrate the effectiveness of developed tracking control scheme using disturbance observer and radial basis function neural network.

Highlights

To facilitate trajectory tracking controller design, a backstepping control strategy is adopted for the small-scale unmanned helicopter system
The disturbance observer and prescribed performance function method are adopted to deal with input saturation and output constraint, respectively
According to theorem 1, the adaptive prescribed performance control schemes are proposed as equations (21), (29), (46), (56), and (63); the disturbance observers of each subsystem are designed as equations (32) and (65); and the neural network (NN) adaptive laws are chosen as equations (31) and (64)

Summary

Introduction

Unmanned helicopters have received a considerable attention and extensive development in the recent years.[1,2,3] Especially, the small-scale unmanned helicopter system with complex dynamics is sensitive to model uncertainty and external disturbance.[4,5,6,7] To deal with this problem, multifarious robust controller design techniques have been developed.[8,9,10] Chen et al.[11] and Choi and Yoo[12] investigated the controller design for the unmanned helicopter using disturbance observer and fuzzy approximator. Zhu and Huo[13] proposed a robust nonlinear adaptive backstepping method for the trajectory tracking control of the model-scaled unmanned helicopter with uncertain parameters. Nonlinear disturbance observer will be introduced to handle the input saturations, and prescribed performance function method will be introduced to handle the output constraints for the unmanned helicopters. “Controller design” section presents the design of adaptive neural tracking controller using the prescribed performance method and nonlinear disturbance observer. The objective of this article is to design a robust adaptive controller subject to input and output constraint for the small-scale helicopter system such that it can track desired trajectories PdðtÞ and dðtÞ. The disturbance observer and prescribed performance function method are adopted to deal with input saturation and output constraint, respectively. Considering the translational dynamic and differentiating dV with respect to time yields d_ V ge[3]

À m gTmr

Conclusion