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Technical Brief

Robust Adaptive Tracking Control of Autonomous Underwater Vehicle-Manipulator Systems

[+] Author and Article Information
Mohan Santhakumar

Mechanical Engineering,
Centre for Robotics and Control,
Indian Institute of Technology Indore,
Madhya Pradesh, India 453-441
e-mail: Santhakumar@iiti.ac.in

Jinwhan Kim

Ocean Systems Engineering,
Ocean Robotics and Intelligence Lab,
Koran Advanced Institute of Science and Technology,
Daejeon, South Korea 305-701
e-mail: jinwhan@kaist.ac.kr

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 5, 2011; final manuscript received March 11, 2014; published online June 12, 2014. Assoc. Editor: Nariman Sepehri.

J. Dyn. Sys., Meas., Control 136(5), 054502 (Jun 12, 2014) (10 pages) Paper No: DS-11-1276; doi: 10.1115/1.4027281 History: Received September 05, 2011; Revised March 11, 2014

This paper proposes a new tracking controller for autonomous underwater vehicle-manipulator systems (UVMSs) using the concept of model reference adaptive control. It also addresses the detailed modeling and simulation of the dynamic coupling between an autonomous underwater vehicle and manipulator system based on Newton–Euler formulation scheme. The proposed adaptation control algorithm is used to estimate the unknown parameters online and compensate for the rest of the system dynamics. Specifically, the influence of the unknown manipulator mass on the control performance is indirectly captured by means of the adaptive control scheme. The effectiveness and robustness of the proposed control scheme are demonstrated using numerical simulations.

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References

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Figures

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Fig. 1

Coordinate frame arrangement of an underwater robot

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Fig. 2

Block diagram of the proposed control scheme for an UVMS

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Fig. 3

Establishment of the manipulator joint coordinate systems

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Fig. 4

Comparative task space (xyz) trajectories (results from the vehicle trajectory tracking an ideal condition)

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Fig. 5

Time histories of the manipulator tip position tracking errors of the PID (an ideal condition)

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Fig. 6

Time histories of the manipulator tip position tracking errors of the MRAC (an ideal condition)

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Fig. 7

Comparative task space (xyz) trajectories (results from the vehicle trajectory tracking an uncertain condition)

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Fig. 8

Time histories of the manipulator tip position tracking errors of the PID (an uncertain condition)

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Fig. 9

Time histories of the manipulator tip position tracking errors of the MRAC (an uncertain condition)

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Fig. 10

Time histories of the manipulator tip position observer errors of the EKF

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Fig. 11

Time histories of the vehicle mass estimate during vehicle tracking

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Fig. 12

Time histories of the damping parameter estimate during vehicle tracking

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Fig. 13

Comparative task space (xyz) trajectories (results from the manipulator trajectory tracking at an uncertain condition)

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Fig. 14

Time histories of the manipulator tip position tracking errors of the PID (an uncertain condition)

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Fig. 15

Time histories of the manipulator tip position tracking errors of the MRAC (an uncertain condition)

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Fig. 16

Time histories of the vehicle thruster speeds (vehicle control inputs)

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Fig. 17

Time histories of the manipulator joint torques (manipulator control inputs)

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