{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:46:06Z","timestamp":1779201966157,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066015"],"award-info":[{"award-number":["62066015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"crossref","award":["2020JJ4511"],"award-info":[{"award-number":["2020JJ4511"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2020JJ4510"],"award-info":[{"award-number":["2020JJ4510"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Foundation of Education Bureau of Hunan Province","award":["20A396"],"award-info":[{"award-number":["20A396"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06465-x","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T06:02:21Z","timestamp":1630908141000},"page":"1329-1343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A gradient-based neural network accelerated for vision-based control of an RCM-constrained surgical endoscope robot"],"prefix":"10.1007","volume":"34","author":[{"given":"Weibing","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luyang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9036-2723","authenticated-orcid":false,"given":"Bolin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"6465_CR1","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1016\/j.conengprac.2013.06.001","volume":"21","author":"Y Zhang","year":"2013","unstructured":"Zhang Y, Li W, Yu M, Wu H, Li J (2013) Encoder based online motion planning and feedback control of redundant manipulators. Control Eng Pract 21:1277\u20131289","journal-title":"Control Eng Pract"},{"key":"6465_CR2","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.aej.2017.01.040","volume":"56","author":"MM Elshabasy","year":"2017","unstructured":"Elshabasy MM, Mohamed KT, Ata AA (2017) Power optimization of planar redundant manipulator moving along constrained-end trajectory using hybrid techniques. Alex Eng J 56:439\u2013447","journal-title":"Alex Eng J"},{"key":"6465_CR3","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s11071-016-2681-9","volume":"85","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Yan X, Chen D, Guo D, Li W (2016) QP-based refined manipulability-maximizing scheme for coordinated motion planning and control of physically constrained wheeled mobile redundant manipulators. Nonlinear Dyn 85:245\u2013261","journal-title":"Nonlinear Dyn"},{"key":"6465_CR4","first-page":"1385","volume-title":"Industrial robotics","author":"M H\u00e4gele","year":"2016","unstructured":"H\u00e4gele M, Nilsson K, Pires JN, Bischoff R (2016) Industrial robotics. Springer International Publishing, Cham, pp 1385\u20131422"},{"key":"6465_CR5","doi-asserted-by":"crossref","unstructured":"Weede O, M\u00f6nnich H, M\u00fcller B, W\u00f6rn H (2011) An intelligent and autonomous endoscopic guidance system for minimally invasive surgery. In: Proceedings of IEEE international conference on robotics and automation, pp. 5762\u20135768","DOI":"10.1109\/ICRA.2011.5980216"},{"key":"6465_CR6","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mart\u00ednez A, Lled\u2019\u00ae L D, Badesa F J, Garc\u00eda N, Sabater-Navarro J M (2014) Integration of heterogeneous robotic systems in a surgical scenario. Proceedings of IEEE RAS\/EMBS International conference on biomedical robotics and biomechatronics. pp. 24\u201327","DOI":"10.1109\/BIOROB.2014.6913746"},{"key":"6465_CR7","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1002\/rcs.453","volume":"8","author":"CH Kuo","year":"2012","unstructured":"Kuo CH, Dai JS, Dasgupta P (2012) Kinematic design considerations for minimally invasive surgical robots: an overview. Int J Med Robot Comput Assist Surg 8:127\u2013145","journal-title":"Int J Med Robot Comput Assist Surg"},{"key":"6465_CR8","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1109\/TCST.2017.2756029","volume":"26","author":"D Guo","year":"2018","unstructured":"Guo D, Xu F, Yan L (2018) New pseudoinverse-based path-planning scheme with PID characteristic for redundant robot manipulators in the presence of noise. IEEE Trans Control Syst Technol 26:2008\u20132019","journal-title":"IEEE Trans Control Syst Technol"},{"key":"6465_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3389\/fnbot.2018.00051","volume":"12","author":"D Guo","year":"2018","unstructured":"Guo D, Xu F, Yan L, Nie Z, Shao H (2018) A new noise-tolerant obstacle avoidance scheme for motion planning of redundant robot manipulators. Front Neurorobot 12:51","journal-title":"Front Neurorobot"},{"key":"6465_CR10","doi-asserted-by":"crossref","unstructured":"Zhao H, Kolathaya S, Ames A D (2014) Quadratic programming and impedance control for transfemoral prosthesis. Proceedings of IEEE international conference on robotics and automation. pp. 1341\u20131347","DOI":"10.1109\/ICRA.2014.6907026"},{"key":"6465_CR11","doi-asserted-by":"crossref","unstructured":"Farshidian F, Jelavi\u0107 E, Winkler A W, Buchli J (2017) Robust whole-body motion control of legged robots. Proceedings of IEEE\/RSJ international conference on intelligent robots and systems. pp. 4589\u20134596","DOI":"10.1109\/IROS.2017.8206328"},{"key":"6465_CR12","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TRO.2015.2418582","volume":"31","author":"F Flacco","year":"2015","unstructured":"Flacco F, De Luca A, Khatib O (2015) Control of redundant robots under hard joint constraints: saturation in the null space. IEEE Trans Robot 31:637\u2013654","journal-title":"IEEE Trans Robot"},{"key":"6465_CR13","volume-title":"Linear programming: theory, algorithms and applications","author":"Y Truma","year":"2014","unstructured":"Truma Y (2014) Linear programming: theory, algorithms and applications. Nova Science Publishers, New York"},{"key":"6465_CR14","volume-title":"Zhang neural networks and neural-dynamic method","author":"Y Zhang","year":"2011","unstructured":"Zhang Y, Yi C (2011) Zhang neural networks and neural-dynamic method. Nova Science Publishers, New York"},{"key":"6465_CR15","doi-asserted-by":"publisher","first-page":"5289","DOI":"10.1109\/TII.2018.2817203","volume":"14","author":"W Li","year":"2018","unstructured":"Li W (2018) A recurrent neural network with explicitly definable convergence time for solving time-variant linear matrix equations. IEEE Trans Ind Inform 14:5289\u20135298","journal-title":"IEEE Trans Ind Inform"},{"key":"6465_CR16","doi-asserted-by":"publisher","first-page":"5330","DOI":"10.1109\/TII.2019.2897803","volume":"15","author":"W Li","year":"2019","unstructured":"Li W, Su Z, Tan Z (2019) A variable-gain finite-time convergent recurrent neural network for time-variant quadratic programming with unknown noises endured. IEEE Trans Ind Inform 15:5330\u20135340","journal-title":"IEEE Trans Ind Inform"},{"key":"6465_CR17","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s00521-017-3010-z","volume":"31","author":"L Xiao","year":"2019","unstructured":"Xiao L (2019) A finite-time convergent Zhang neural network and its application to real-time matrix square root finding. Neural Comput Appl 31:793\u2013800","journal-title":"Neural Comput Appl"},{"key":"6465_CR18","doi-asserted-by":"publisher","first-page":"3636","DOI":"10.1016\/j.jfranklin.2020.02.024","volume":"357","author":"Y Shi","year":"2020","unstructured":"Shi Y, Jin L, Li S, Qiang J (2020) Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation. J Frankl Inst 357:3636\u20133655","journal-title":"J Frankl Inst"},{"key":"6465_CR19","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/72.950137","volume":"12","author":"Y Leung","year":"2001","unstructured":"Leung Y, Chen K-Z, Jiao Y-C, Gao X-B, Leung KS (2001) A new gradient-based neural network for solving linear and quadratic programming problems. IEEE Trans Neural Netw 12:1074\u20131083","journal-title":"IEEE Trans Neural Netw"},{"key":"6465_CR20","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1023\/A:1011245911067","volume":"19","author":"Q Han","year":"2001","unstructured":"Han Q, Liao L-Z, Qi H, Qi L (2001) Stability analysis of gradient-based neural networks for optimization problems. J Global Optim 19:363\u2013381","journal-title":"J Global Optim"},{"key":"6465_CR21","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge"},{"key":"6465_CR22","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1109\/TMECH.2017.2683561","volume":"22","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Zheng L, Yu J, Li Y, Yu Z (2017) Three recurrent neural networks and three numerical methods for solving a repetitive motion planning scheme of redundant robot manipulators. IEEE\/ASME Trans Mechatron 22:1423\u20131434","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"6465_CR23","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.1109\/TAC.2018.2872201","volume":"64","author":"D Liao-McPherson","year":"2019","unstructured":"Liao-McPherson D, Huang M, Kolmanovsky I (2019) A regularized and smoothed Fischer-Burmeister method for quadratic programming with applications to model predictive control. IEEE Trans Autom Control 64:2937\u20132944","journal-title":"IEEE Trans Autom Control"},{"key":"6465_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neunet.2012.12.009","volume":"39","author":"S Li","year":"2013","unstructured":"Li S, Li Y, Wang Z (2013) A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application. Neural Netw 39:27\u201339","journal-title":"Neural Netw"},{"key":"6465_CR25","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.neunet.2020.07.033","volume":"131","author":"H Su","year":"2020","unstructured":"Su H, Hu Y, Karimi HR, Knoll A, Ferrigno G, Momi ED (2020) Improved recurrent neural network-based manipulator control with remote center of motion constraints: experimental results. Neural Netw 131:291\u2013299","journal-title":"Neural Netw"},{"key":"6465_CR26","doi-asserted-by":"publisher","first-page":"132203","DOI":"10.1007\/s11432-019-2735-6","volume":"64","author":"AH Khan","year":"2021","unstructured":"Khan AH, Li S, Cao X (2021) Tracking control of redundant manipulator under active remote center-of-motion constraints: an RNN-based metaheuristic approach. Sci China Inform Sci 64:132203","journal-title":"Sci China Inform Sci"},{"key":"6465_CR27","doi-asserted-by":"publisher","first-page":"5272","DOI":"10.1109\/TNNLS.2020.2965553","volume":"31","author":"W Li","year":"2020","unstructured":"Li W, Chiu PWY, Li Z (2020) An accelerated finite-time convergent neural network for visual servoing of a flexible surgical endoscope with physical and RCM constraints. IEEE Trans Neural Netw Learn Syst 31:5272\u20135284","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6465_CR28","doi-asserted-by":"crossref","unstructured":"Zhang X, Li W, Ng W Y, Huang Y, Xian Y, Chiu P W Y, Li Z (2021) An autonomous robotic flexible endoscope system with a DNA-inspired continuum mechanism. Proceedings of international conference on robotics and automation (ICRA), Accepted","DOI":"10.1109\/ICRA48506.2021.9561651"},{"key":"6465_CR29","doi-asserted-by":"publisher","first-page":"2915","DOI":"10.1007\/s12555-017-0486-3","volume":"16","author":"H Su","year":"2018","unstructured":"Su H, Sandoval J, Vieyres P, Poisson G, Ferrigno G, Momi ED (2018) Safety-enhanced collaborative framework for tele-operated minimally invasive surgery using a 7-DoF torque-controlled robot. Int J Contr Autom Syst 16:2915\u20132923","journal-title":"Int J Contr Autom Syst"},{"key":"6465_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20144-8","volume-title":"Robotics, vision and control: fundamental algorithms in MATLAB","author":"P Corke","year":"2011","unstructured":"Corke P (2011) Robotics, vision and control: fundamental algorithms in MATLAB. Springer, Berlin"},{"key":"6465_CR31","doi-asserted-by":"crossref","unstructured":"Marinho M M, Harada K, Mitsuishi M (2017) Comparison of remote center-of-motion generation algorithms. Proceedings of IEEE\/SICE international symposium on system integration. pp. 668\u2013673","DOI":"10.1109\/SII.2017.8279298"},{"key":"6465_CR32","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S0375-9601(02)00424-3","volume":"298","author":"Y Zhang","year":"2002","unstructured":"Zhang Y, Wang J (2002) A dual neural network for convex quadratic programming subject to linear equality and inequality constraints. Phys Lett A 298:271\u2013278","journal-title":"Phys Lett A"},{"key":"6465_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199653126.001.0001","volume-title":"The macroeconomic theory of exchange rate crises","author":"G Piersanti","year":"2012","unstructured":"Piersanti G (2012) The macroeconomic theory of exchange rate crises. Oxford University Press, Oxford"},{"key":"6465_CR34","doi-asserted-by":"publisher","first-page":"3195","DOI":"10.1109\/TCYB.2019.2906263","volume":"50","author":"W Li","year":"2020","unstructured":"Li W, Xiao L, Liao B (2020) A finite-time convergent and noise-rejection recurrent neural network and its discretization for dynamic nonlinear equations solving. IEEE Trans Cybern 50:3195\u20133207","journal-title":"IEEE Trans Cybern"},{"key":"6465_CR35","volume-title":"Geometric methods and applications: for computer science and engineering","author":"J Gallier","year":"2020","unstructured":"Gallier J (2020) Geometric methods and applications: for computer science and engineering. Springer, New York"},{"key":"6465_CR36","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.neucom.2014.06.018","volume":"143","author":"P Miao","year":"2014","unstructured":"Miao P, Shen Y, Xia X (2014) Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application. Neurocomputing 143:80\u201389","journal-title":"Neurocomputing"},{"key":"6465_CR37","doi-asserted-by":"crossref","unstructured":"Freese M, Singh S, Ozaki F, Matsuhira N (2010) Virtual robot experimentation platform V-REP: A versatile 3-D robot simulator. Proceedings of international conference on simulation, modeling, and programming for autonomous robots. pp. 51\u201362","DOI":"10.1007\/978-3-642-17319-6_8"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06465-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06465-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06465-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T15:29:34Z","timestamp":1642778974000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06465-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,6]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6465"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06465-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,6]]},"assertion":[{"value":"6 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}