Adaptive Sliding Mode Tracking Control for Unmanned Autonomous Helicopters Based on Neural Networks

Min Wan,Kun Yan,Mou Chen

doi:10.1155/2018/7379680

Abstract

In this paper, an adaptive sliding mode tracking control scheme is developed for the medium-scale unmanned autonomous helicopter with system uncertainties and external unknown disturbances. A simplified mathematical model is established, which is divided into position subsystem and attitude subsystem. The uncertainty term of the system is handled by the inherent approximation ability of the neural network. The sliding model control scheme under the backstepping frame is developed for tackling disturbances. The stability of the simplified system is proved by using the Lyapunov theory, and the tracking errors are guaranteed to be uniformly bounded. Numerical simulation results show that the proposed control strategy is effective.

Highlights

The unique flight capabilities of unmanned autonomous helicopters (UAHs), such as vertical take-off and landing, hovering, low-altitude flying, make them have an irreplaceable position in military and civil fields
The rest of this paper is organized as follows: in Section 2, the UAH model with uncertainties and disturbances is established; in Section 3, the design processes of the adaptive sliding mode control (SMC) method with neural network (NN) are presented and stability analysis is carried out to guarantee the feasibility of the proposed controller; Section 4 provides simulations to verify the effectiveness of the proposed control method; and Section 5 concludes the paper
Similar to the position system, the control law based on SMC and radical basis function NN (RBFNN) will be designed to achieve the purpose of tracking the reference attitude signal

Summary

Introduction

The unique flight capabilities of unmanned autonomous helicopters (UAHs), such as vertical take-off and landing, hovering, low-altitude flying, make them have an irreplaceable position in military and civil fields. In [6], a flight controller with the approximate feedback linearization method and active disturbance rejection control was designed for the helicopter, which had good robustness. In [7], a nonlinear model predictive control combined with the disturbance observer was proposed for helicopter tracking control. In [16], two nonlinear control methods were compared and adaptive SMC can handle sensor noise and model uncertainty better than feedback linearization control. The rest of this paper is organized as follows: in Section 2, the UAH model with uncertainties and disturbances is established; in Section 3, the design processes of the adaptive SMC method with NNs are presented and stability analysis is carried out to guarantee the feasibility of the proposed controller; Section 4 provides simulations to verify the effectiveness of the proposed control method; and Section 5 concludes the paper

Problem Formulation and Preliminaries

Adaptive Sliding Mode Tracking Controller Design

Simulation Results

Conclusion