基于模糊补偿的深海作业级远程操控潜水器#br#
自适应位姿控制

上海交通大学学报（自然版） ›› 2017, Vol. 51 ›› Issue (4): 403-.

基于模糊补偿的深海作业级远程操控潜水器#br# 自适应位姿控制

霍星星，葛彤，王旭阳

上海交通大学船舶海洋与建筑工程学院，上海 200240

出版日期:2017-04-03 发布日期:2017-04-03
基金资助:
国家高技术研究发展计划（863）项目（2012AA092101）资助

Adaptive Position and Attitude Control for Deep Sea WorkClass#br# Remotely Operated Underwater Vehicle Based on Fuzzy Compensation

HUO Xingxing,GE Tong,WANG Xuyang

School of Naval Architecture, Ocean and Civil Engineering,
Shanghai Jiao Tong University, Shanghai 200240, China

Online:2017-04-03 Published:2017-04-03

摘要/Abstract

摘要：

摘要：针对参数变化、流以及其他未知干扰对深海作业级远程操控潜水器(ROV)位姿控制的影响，设计了基于模糊补偿的ROV自适应位姿控制器.从大地坐标系下的ROV系统模型中分离出由于参数变化、流以及其他未知因素所产生的干扰力/力矩，并分析了干扰力/力矩的变化特性，利用模糊逻辑系统(FLS)进行逼近，设计基于干扰力/力矩模糊补偿的ROV自适应位姿控制器；同时，为了消除逼近误差的影响，设计了稳健自适应控制律.结果表明：FLS能够以较高的精度逼近ROV所受到的干扰力/力矩；所设计的基于模糊补偿的深海作业级ROV自适应位姿控制器具有良好的跟踪性能、抗干扰能力和稳健性.

关键词: 远程操控潜水器, 流干扰, 模糊逻辑系统, 位姿控制

Abstract:

Abstract： To overcome the impact of parameter variations, current and other unknown disturbance on the position and attitude control of deep sea workclass remotely operated underwater vehicle (ROV), an adaptive position and attitude controller for the ROV was designed based on fuzzy compensation. The disturbance force and moment generated by parameter variations，current and other unknown disturbance were isolated from mathematical model of ROV in the fixed coordinate system, and a fuzzy logic system (FLS) was used to approximate the force and moment. An adaptive position and attitude controller for the ROV based on fuzzy compensation of the force and moment was designed. In order to decrease the effect of approximation error we also applied the robust adaptive control law. The results of numerical simulation indicated that the disturbance force and moment acting on ROV can be approximated with high precision by using FLS, and the designed controller achieves strong antidisturbance ability, tracking performance and robustness.

Key words: remotely operated underwater vehicle (ROV), current disturbance, fuzzy logic system, position and attitude control

中图分类号:

P 754.3

霍星星，葛彤，王旭阳. 基于模糊补偿的深海作业级远程操控潜水器#br# 自适应位姿控制[J]. 上海交通大学学报（自然版）, 2017, 51(4): 403-.

HUO Xingxing,GE Tong,WANG Xuyang. Adaptive Position and Attitude Control for Deep Sea WorkClass#br# Remotely Operated Underwater Vehicle Based on Fuzzy Compensation[J]. Journal of Shanghai Jiaotong University, 2017, 51(4): 403-.

参考文献

［1］TEHRANI N H, HEIDARI M, ZAKERI Y, et al. Development, depth control and stability analysis of an underwater remotely operated vehicle (ROV) ［C］∥8th IEEE International Conference on Control and Automation (ICCA). Xiamen: IEEE, 2010: 814819.
［2］CONRADO DE SOUZA E, MARUYAMA N. Intelligent UUVs: Some issues on ROV dynamic positioning［J］. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 214226.
［3］GOMES R M F, SOUSA J B, PEREIRA F L, et al. Integrated maneuver and control design for ROV operations［C］∥Proceedings of IEEE OCEANS 2003. San Diego, USA: IEEE, 2003: 703710.
［4］ZHU K W, GU L Y. A MIMO nonlinear robust controller for workclass ROVs positioning and trajectory tracking control［C］∥Chinese Control and Decision Conference (CCDC). Mianyang, China: IEEE, 2011:25652570.
［5］DENG C N, GE T. Modelfree high order sliding controller for underwater vehicle with transient process［J］. Advanced Materials Research, 2012, 591/592/593: 11841190.
［6］XIA G Q, PANG C C, WANG H J, et al. Adaptive neural network controller applied to dynamic positioning of a remotely operated vehicle［C］∥2013 MTS/IEEE Oceans. Bergen, Norway: IEEE, 2013.
［7］JAVADIMOGHADDAM J, BAGHERI A. An adaptive neurofuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle［J］. Expert Systems with Applications, 2010, 37(1): 647660.
［8］BAGHERI A, MOGHADDAM J J. Simulation and tracking control based on neuralnetwork strategy and slidingmode control for underwater remotely operated vehicle［J］. Neurocomputing, 2009, 72(7/8/9): 19341950.
［9］BESSA W M, DUTRA M S, KREUZER E. An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles robotics and autonomous systems［J］. Robotics and Autonomous Systems, 2010, 58(1): 1626.
［10］BESSA W M, DUTRA M S, KREUZER E. Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller［J］. Robotics and Autonomous Systems, 2008, 56(8): 670677.
［11］GUO J, CHIU F C, HUANG C C. Design of a sliding mode fuzzy controller for the guidance and control of an autonomous underwater vehicle［J］. Ocean Engineering, 2003, 30(16): 21372155.
［12］SAGHAFI M H, KASHANI H, MOZAYANI N, et al. Developing a tracking algorithm for underwater ROV using fuzzy logic controller［C］∥5th Iranian Conference on Fuzzy Systems. Tehran, Iran: Sahand University of Technology, 2004:1725.
［13］LABIOD S, GUERRA T M. Direct adaptive fuzzy control for a class of MIMO nonlinear systems［J］. International Journal of Systems Science, 2007, 38(8): 665675.
［14］ALQASEMI R, MATSUNAGA K. Advances in robot manipulators［M］. Rijeka, Croatia: InTech, 2010: 279298.
［15］YOO B K, HAM W C. Adaptive control of robot manipulator using fuzzy compensator［J］. IEEE Transactions on Fuzzy Systems, 2000, 8(2): 186199.
［16］FOSSEN T I. Guidance and control of ocean vehicles［M］. Chichester, England: Wiley, 1994.
［17］COSTA R R, LIZARRALDE F, CUNHA J D. Dynamic positioning of remotely operated underwater vehicles［J］. IEEE Robotics & Amp Automation Magazine, 2000, 7(7): 2131.
［18］朱康武.作业型ROV多变量位姿稳健控制方法研究［D］. 杭州: 浙江大学机械工程学院, 2012.
［19］王立新.模糊系统与模糊控制教程［M］. 北京: 清华大学出版社, 2003.
［20］刘金琨.先进PID控制MATLAB仿真［M］.北京:电子工业出版社, 2011.