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Adaptive Sliding Mode Control for Depth Trajectory Tracking of Remotely Operated Vehicle with Thruster Nonlinearity

Published online by Cambridge University Press:  28 July 2016

Zhenzhong Chu ,
Daqi Zhu ,
Simon X. Yang  and
Gene Eu Jan
Zhenzhong Chu*
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, 201306, China)
Daqi Zhu
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, 201306, China)
Simon X. Yang
Affiliation:
(The Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON. N1G2W1, Canada)
Gene Eu Jan
Affiliation:
(Department of Computer Science and Information Engineering, National Taipei University, Taipei County, 237, Taiwan, ROC)
*
(E-mail: zdq367@aliyun.com)
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Abstract

This paper focuses on depth trajectory tracking control for a Remotely Operated Vehicle (ROV) with dead-zone nonlinearity and saturation nonlinearity of thruster; an adaptive sliding mode control method based on neural network is proposed. Through the analysis of dead-zone nonlinearity and saturation nonlinearity of thruster, the depth trajectory tracking control system model of a ROV which uses thruster control signals as system input has been established. According to the principle of sliding mode control, an adaptive sliding mode depth trajectory tracking controller is built by using three-layer feed-forward neural network for online identification of unknown items. The selection method and update laws of the control parameters are also given. The uniform ultimate boundedness of trajectory tracking error is analysed by Lyapunov theorem. Finally, the effectiveness of the proposed method is illustrated by simulations.

Keywords

Remotely operated vehicleTrajectory trackingAdaptive controlSliding mode controlThruster nonlinearity
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 1 , January 2017 , pp. 149 - 164
Copyright
Copyright © The Royal Institute of Navigation 2016 

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