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Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach

Ameer Hamza Khan Orcid Logo, Shuai Li Orcid Logo, Dechao Chen Orcid Logo, Liefa Liao

Neurocomputing, Volume: 400, Pages: 272 - 284

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.neucom.2020.02.109

Abstract

In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a metaheuristic optimization algorithm for the tracking control of mobile-manipulator while enforcing nonholonomic constraints. Traditional approaches for tracking control of mobile robots usually require the computation...

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Published in: Neurocomputing
ISSN: 0925-2312
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53900
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spelling 2023-03-17T11:28:40.0248949 v2 53900 2020-04-06 Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-04-06 MECH In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a metaheuristic optimization algorithm for the tracking control of mobile-manipulator while enforcing nonholonomic constraints. Traditional approaches for tracking control of mobile robots usually require the computation of Jacobian-inverse or linearization of its mathematical model. The proposed algorithm uses a nature-inspired optimization approach to directly solve the nonlinear optimization problem without any further transformation. First, we formulate the tracking control as a constrained optimization problem. The optimization problem is formulated on position-level to avoid the computationally expensive Jacobian-inversion. The nonholonomic limitation is ensured by adding equality constraints to the formulated optimization problem. We then present the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm to solve the optimization problem efficiently using very few mathematical operations. We present a theoretical analysis of the proposed algorithm and show that its computational cost is linear with respect to the degree of freedoms (DOFs), i.e., O(m). Additionally, we also prove its stability and convergence. Extensive simulation results are prepared using a simulated model of IIWA14, a 7-DOF industrial-manipulator, mounted on a differentially driven cart. Comparison results with particle swarm optimization (PSO) algorithm are also presented to prove the accuracy and numerical efficiency of the proposed controller. The results demonstrate that the proposed algorithm is several times (around 75 in the worst case) faster in execution as compared to PSO, and suitable for real-time implementation. The tracking results for three different trajectories; circular, rectangular, and rhodonea paths are presented. Journal Article Neurocomputing 400 272 284 Elsevier BV 0925-2312 Mobile-manipulator, Tracking control, Metaheuristic optimization, Recurrent neural network, Nature-inspired algorithm, Redundancy resolution 4 8 2020 2020-08-04 10.1016/j.neucom.2020.02.109 http://dx.doi.org/10.1016/j.neucom.2020.02.109 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2023-03-17T11:28:40.0248949 2020-04-06T09:35:35.2884631 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ameer Hamza Khan 0000-0002-5367-5277 1 Shuai Li 0000-0001-8316-5289 2 Dechao Chen 0000-0002-5171-1414 3 Liefa Liao 4 53900__17084__19d82817f881499c8643a6d7c46cd5fb.pdf 53900.pdf 2020-04-17T13:04:25.6587995 Output 11926143 application/pdf Version of Record true 2021-03-12T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
spellingShingle Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
Shuai Li
title_short Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
title_full Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
title_fullStr Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
title_full_unstemmed Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
title_sort Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ameer Hamza Khan
Shuai Li
Dechao Chen
Liefa Liao
format Journal article
container_title Neurocomputing
container_volume 400
container_start_page 272
publishDate 2020
institution Swansea University
issn 0925-2312
doi_str_mv 10.1016/j.neucom.2020.02.109
publisher Elsevier BV
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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
url http://dx.doi.org/10.1016/j.neucom.2020.02.109
document_store_str 1
active_str 0
description In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a metaheuristic optimization algorithm for the tracking control of mobile-manipulator while enforcing nonholonomic constraints. Traditional approaches for tracking control of mobile robots usually require the computation of Jacobian-inverse or linearization of its mathematical model. The proposed algorithm uses a nature-inspired optimization approach to directly solve the nonlinear optimization problem without any further transformation. First, we formulate the tracking control as a constrained optimization problem. The optimization problem is formulated on position-level to avoid the computationally expensive Jacobian-inversion. The nonholonomic limitation is ensured by adding equality constraints to the formulated optimization problem. We then present the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm to solve the optimization problem efficiently using very few mathematical operations. We present a theoretical analysis of the proposed algorithm and show that its computational cost is linear with respect to the degree of freedoms (DOFs), i.e., O(m). Additionally, we also prove its stability and convergence. Extensive simulation results are prepared using a simulated model of IIWA14, a 7-DOF industrial-manipulator, mounted on a differentially driven cart. Comparison results with particle swarm optimization (PSO) algorithm are also presented to prove the accuracy and numerical efficiency of the proposed controller. The results demonstrate that the proposed algorithm is several times (around 75 in the worst case) faster in execution as compared to PSO, and suitable for real-time implementation. The tracking results for three different trajectories; circular, rectangular, and rhodonea paths are presented.
published_date 2020-08-04T04:07:07Z
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score 11.013082

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