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Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer

Runxian Yang, Chenguang Yang, Mou Chen, Jing Na

Systems Science & Control Engineering, Volume: 5, Issue: 1, Pages: 287 - 300

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1080/21642583.2017.1347532

Abstract

In this paper, an adaptive impedance control combined with disturbance observer (DOB) is developed for a general class of uncertain robot manipulators in discrete time. The impedance control is applied to realize the interaction force control of robot manipulators in unknown, time-varying environmen...

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Published in: Systems Science & Control Engineering
ISSN: 2164-2583
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa34594
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first_indexed 2017-07-11T12:06:31Z
last_indexed 2018-04-12T19:06:41Z
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spelling 2018-04-12T16:08:27.9751164 v2 34594 2017-07-10 Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2017-07-10 EEN In this paper, an adaptive impedance control combined with disturbance observer (DOB) is developed for a general class of uncertain robot manipulators in discrete time. The impedance control is applied to realize the interaction force control of robot manipulators in unknown, time-varying environments. The optimal reference trajectory is produced by impedance control, and the impedance parameters are achieved using Q-learning technique, which is implemented based on trajectory tracking errors. The position control with DOB of robot manipulators is implemented to track the virtual desired trajectory, and the DOB is designed to compensate for unknown compounded disturbance function by bounding both tracking error inputs and compounded disturbance inputs in a permitted control region, of which the compounded disturbance function is taken into account of all uncertain terms and external disturbances. The appropriate DOB parameters are selected applying linear matrix inequalities (LMIs) method. Both the impedance control and the bounded DOB control can well guarantee semiglobal uniform boundness of the closed-loop robot systems based on Lyapunov analysis and Schur complement theory. Simulation results are performed to test and verify effectiveness of the investigated combining adaptive impedance control with DOB. Journal Article Systems Science & Control Engineering 5 1 287 300 2164-2583 Discrete-time system, time-varying environment, robot manipulator, disturbance observer, Trajectory tracking, interaction control 31 12 2017 2017-12-31 10.1080/21642583.2017.1347532 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-04-12T16:08:27.9751164 2017-07-10T15:20:53.3297295 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Runxian Yang 1 Chenguang Yang 2 Mou Chen 3 Jing Na 4 0034594-10072017152347.pdf SSCE17DOB.pdf 2017-07-10T15:23:47.3970000 Output 2341996 application/pdf Version of Record true 2017-07-10T00:00:00.0000000 true eng
title Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
spellingShingle Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
Chenguang Yang
title_short Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
title_full Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
title_fullStr Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
title_full_unstemmed Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
title_sort Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Runxian Yang
Chenguang Yang
Mou Chen
Jing Na
format Journal article
container_title Systems Science & Control Engineering
container_volume 5
container_issue 1
container_start_page 287
publishDate 2017
institution Swansea University
issn 2164-2583
doi_str_mv 10.1080/21642583.2017.1347532
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description In this paper, an adaptive impedance control combined with disturbance observer (DOB) is developed for a general class of uncertain robot manipulators in discrete time. The impedance control is applied to realize the interaction force control of robot manipulators in unknown, time-varying environments. The optimal reference trajectory is produced by impedance control, and the impedance parameters are achieved using Q-learning technique, which is implemented based on trajectory tracking errors. The position control with DOB of robot manipulators is implemented to track the virtual desired trajectory, and the DOB is designed to compensate for unknown compounded disturbance function by bounding both tracking error inputs and compounded disturbance inputs in a permitted control region, of which the compounded disturbance function is taken into account of all uncertain terms and external disturbances. The appropriate DOB parameters are selected applying linear matrix inequalities (LMIs) method. Both the impedance control and the bounded DOB control can well guarantee semiglobal uniform boundness of the closed-loop robot systems based on Lyapunov analysis and Schur complement theory. Simulation results are performed to test and verify effectiveness of the investigated combining adaptive impedance control with DOB.
published_date 2017-12-31T03:42:55Z
_version_ 1763751992730058752
score 11.036706

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