Constrained Control of Autonomous Underwater Vehicles Based on Command Optimization and Disturbance Estimation

In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater envir...

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Published in	IEEE transactions on industrial electronics (1982) Vol. 66; no. 5; pp. 3627 - 3635
Main Authors	Peng, Zhouhua, Wang, Jiasen, Wang, Jun
Format	Journal Article
Language	English
Published	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Autonomous underwater vehicles Autonomous underwater vehicles (AUVs) command governor (CG) Computer simulation Constraints Control systems design disturbance observer (DO) Disturbance observers Environment models Estimation finite-time convergence Governors Kinetic theory Military technology Neurodynamics Ocean currents Ocean models Oceans one-layer recurrent neural network Optimization Parameter uncertainty Parametric statistics Quadratic programming receding horizon optimization Recurrent neural networks Uncertainty
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Abstract	In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater environment. An antidisturbance constrained controller is developed by designing a command governor and a disturbance observer. Specifically, the disturbance observer is developed to estimate the lumped disturbance composed of parametric model uncertainties, modeling errors, and unknown environmental forces. The command governor is designed for optimizing command signals in the receding horizon within the state and input constraints. The command governor is formulated as a quadratically constrained quadratic programming problem. To facilitate online implementations, a neurodynamic optimization method based on a one-layer recurrent neural network is employed for solving the quadratic optimization problem subject to inequality constraints with finite-time convergence. The efficacy of the proposed antidisturbance constrained control method for autonomous underwater vehicles is substantiated via simulations and comparisons.
AbstractList	In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater environment. An antidisturbance constrained controller is developed by designing a command governor and a disturbance observer. Specifically, the disturbance observer is developed to estimate the lumped disturbance composed of parametric model uncertainties, modeling errors, and unknown environmental forces. The command governor is designed for optimizing command signals in the receding horizon within the state and input constraints. The command governor is formulated as a quadratically constrained quadratic programming problem. To facilitate online implementations, a neurodynamic optimization method based on a one-layer recurrent neural network is employed for solving the quadratic optimization problem subject to inequality constraints with finite-time convergence. The efficacy of the proposed antidisturbance constrained control method for autonomous underwater vehicles is substantiated via simulations and comparisons.
Author	Peng, Zhouhua Wang, Jiasen Wang, Jun
Author_xml	– sequence: 1 givenname: Zhouhua orcidid: 0000-0003-4468-7281 surname: Peng fullname: Peng, Zhouhua email: zhpeng@dlmu.edu.cn organization: School of Marine Electrical Engineering and with the Collaborative Innovation Institute of Unmanned Ships, Dalian Maritime University, Dalian, China – sequence: 2 givenname: Jiasen orcidid: 0000-0001-5687-5947 surname: Wang fullname: Wang, Jiasen email: jwang.cs@cityu.edu.hk organization: Department of Computer Science and the School of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong – sequence: 3 givenname: Jun orcidid: 0000-0002-1305-5735 surname: Wang fullname: Wang, Jun email: jiasewang2-c@my.cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong
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Snippet	In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The...
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SubjectTerms	Autonomous underwater vehicles Autonomous underwater vehicles (AUVs) command governor (CG) Computer simulation Constraints Control systems design disturbance observer (DO) Disturbance observers Environment models Estimation finite-time convergence Governors Kinetic theory Military technology Neurodynamics Ocean currents Ocean models Oceans one-layer recurrent neural network Optimization Parameter uncertainty Parametric statistics Quadratic programming receding horizon optimization Recurrent neural networks Uncertainty
Title	Constrained Control of Autonomous Underwater Vehicles Based on Command Optimization and Disturbance Estimation
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