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Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

Hanzhen Xiao, Zhijun Li, Chenguang Yang, Lixian Zhang, Peijiang Yuan, Liang Ding, Tianmiao Wang

IEEE Transactions on Industrial Electronics, Volume: 64, Issue: 1, Pages: 505 - 516

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1109/tie.2016.2606358

Abstract

In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation i...

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Published in: IEEE Transactions on Industrial Electronics
ISSN: 0278-0046 1557-9948
Published: Institute of Electrical and Electronics Engineers (IEEE) 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa29909
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first_indexed 2016-09-12T18:58:08Z
last_indexed 2020-08-03T12:46:32Z
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spelling 2020-08-03T10:25:51.9602664 v2 29909 2016-09-12 Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-09-12 EEN In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation into the control input with an additional exponential decaying term. An MPC based control method is then designed for the robot in the presence of external disturbances. The MPC optimization has been formulated as a convex nonlinear minimization problem and a primal-dual neural network (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been significantly improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach, which can be applied for a large range of wheeled mobile robots. Journal Article IEEE Transactions on Industrial Electronics 64 1 505 516 Institute of Electrical and Electronics Engineers (IEEE) 0278-0046 1557-9948 1 1 2017 2017-01-01 10.1109/tie.2016.2606358 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2020-08-03T10:25:51.9602664 2016-09-12T16:19:55.1037834 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Hanzhen Xiao 1 Zhijun Li 2 Chenguang Yang 3 Lixian Zhang 4 Peijiang Yuan 5 Liang Ding 6 Tianmiao Wang 7 0029909-12092016162445.pdf ALL_15-TIE-1024R5.pdf 2016-09-12T16:24:45.6130000 Output 979545 application/pdf Accepted Manuscript true 2016-09-12T00:00:00.0000000 false
title Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
spellingShingle Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
Chenguang Yang
title_short Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
title_full Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
title_fullStr Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
title_full_unstemmed Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
title_sort Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Hanzhen Xiao
Zhijun Li
Chenguang Yang
Lixian Zhang
Peijiang Yuan
Liang Ding
Tianmiao Wang
format Journal article
container_title IEEE Transactions on Industrial Electronics
container_volume 64
container_issue 1
container_start_page 505
publishDate 2017
institution Swansea University
issn 0278-0046
1557-9948
doi_str_mv 10.1109/tie.2016.2606358
publisher Institute of Electrical and Electronics Engineers (IEEE)
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, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation into the control input with an additional exponential decaying term. An MPC based control method is then designed for the robot in the presence of external disturbances. The MPC optimization has been formulated as a convex nonlinear minimization problem and a primal-dual neural network (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been significantly improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach, which can be applied for a large range of wheeled mobile robots.
published_date 2017-01-01T03:36:28Z
_version_ 1763751586297806848
score 11.036706

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