Anti-Disturbance Neural-Sliding Mode Control for Inertially Stabilized Platform With Actuator Saturation

Zhushun Ding,Yuedong Lang,Feng Zhao,Zhe Jiang,Jiajing Zhu

doi:10.1109/access.2019.2927427

Abstract

To consider the environment during ground vehicle driving, the inertially stabilized platform (ISP) can be used for electro-optical tracking instruments to isolate the senor's line of sight (LOS) from the carrier's vibrations with high precision and stability. This paper proposes the combination of a backstepping sliding mode controller with the adaptive neural networks approach (BSMC-NN) for ISP that achieves output torque saturation and considers parametric uncertainties, friction, and gimbal mass imbalance. An adaptive radial basis function neural network is adopted to approximate uncertain disturbances in this dynamic system. In contrast to the existing saturated control structures, an auxiliary function is designed to compensate for any error between the designed and the actual control torque. The closed-loop stability and asymptotic convergence performance are guaranteed based on the Lyapunov stability theory. Finally, the simulation and experimental results demonstrate that this proposed controller can effectively regulate the gimbal rotation angle under different external disturbances. This offers superior control performance despite the existence of the nonlinear dynamics and control input constraints.

Highlights

Due to increasing expectations for unmanned intelligence, surveillance, and reconnaissance systems, the inertially stabilized platform (ISP) attracted significant interest in the aircraft vehicle industry [1], [2]
This paper proposes a backstepping sliding mode controller combined with an adaptive neural networks approach (BSMC-NN) for ISP that has output torque saturation and a number of nonlinear factors;
PROBLEM STATEMENT ISP can be used to switch several times between two working modes, attitude servo-control and attitude locking; this study focuses on the angle velocity ωPx and ωAz

Summary

INTRODUCTION

Due to increasing expectations for unmanned intelligence, surveillance, and reconnaissance systems, the inertially stabilized platform (ISP) attracted significant interest in the aircraft vehicle industry [1], [2]. Several parameters, such as the rotational inertia of the platform, are assumed to have a fixed value, which is not the case for physical systems in reality when the camera adjusts the focal length Motivated by these observations, this paper proposes a backstepping sliding mode controller combined with an adaptive neural networks approach (BSMC-NN) for ISP that has output torque saturation and a number of nonlinear factors; (e.g, friction significantly affects the system behavior). To alleviate the presence of actuator saturation, an auxiliary design function is employed to compensate for any error between the designed and the actual control torque Both the update laws of the weighting matrix and the asymptotic stability of the closed loop system are derived based on the Lyapunov stability theory. The parameter torque constant KT and the resistance of the motor Ra are provided by the motor manufacturer

MODELING FOR MASS IMBALANCE DISTURBANCE

ANTI-DISTURBANCE NEURAL NETWORK SMC

SIMULATIONS AND EXPERIMENT

Findings

CONCLUSIONS