{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:37:46Z","timestamp":1777703866142,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,2,15]],"date-time":"2017-02-15T00:00:00Z","timestamp":1487116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p>This paper proposes an optimized fuzzy reinforcement-learning algorithm to control ionic polymer metal composites. The IPMC has been made by thin polymer membrane with metal electrodes plated chemically on the both faces. Its application is widely and may be used as the artificial muscle due to the large bending strain at low voltages. Although there are some controllers designed in the literature, most of them are model-based and for this reason are not used widely. In this study, a free model controller based on fuzzy is considered. The fuzzy rule making is not straightforward and must be taken by an expert, so an algorithm based on the reinforcement learning is employed to make the rule sets strongly. After learning the fuzzy sets, firstly, the reinforcement learning parameters have been optimized using the Taguchi method and then an optimized algorithm based on the genetic is started to tune up the configuration of membership functions for controller designing. The effectiveness of the reported controller for the IPMC actuator is confirmed by simulation and experimental results.<\/jats:p>","DOI":"10.3233\/jifs-161211","type":"journal-article","created":{"date-parts":[[2017,2,17]],"date-time":"2017-02-17T10:35:22Z","timestamp":1487327722000},"page":"125-136","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["An intelligent controller for ionic polymer metal composites using optimized fuzzy reinforcement learning"],"prefix":"10.1177","volume":"33","author":[{"given":"Masoud","family":"Goharimanesh","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elyas","family":"Abbasi Jannatabadi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossein","family":"Moeinkhah","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Sistan and Baluchestan, Zahedan, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Bagher","family":"Naghibi-Sistani","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Akbar","family":"Akbari","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,2,15]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.matlet.2014.03.115","article-title":"Ex-situ temperature & humidity aging effect on the tensile behavior of Nafion N117 membrane used in Ionic Polymer-Metal Composite actuators","author":"Xiao Y.","year":"2014","unstructured":"XiaoY., PoorneshK.K., WendlingL. and ChoC.Ex-situ temperature & humidity aging effect on the tensile behavior of Nafion N117 membrane used in Ionic Polymer-Metal Composite actuators, Materials Letters (2014).","journal-title":"Materials Letters"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4028\/www.scientific.net\/JBBTE.19.13","article-title":"Grasshopper knee joint-torque analysis of actuators using ionic polymer metal composites (IPMC)","volume":"19","author":"Farid M.","year":"2014","unstructured":"FaridM., GangZ., Linh KhuongT. and SunZ.Z., Grasshopper knee joint-torque analysis of actuators using ionic polymer metal composites (IPMC), Journal of Biomimetics, Biomaterials, and Tissue Engineering19 (2014), 13\u201323.","journal-title":"Journal of Biomimetics, Biomaterials, and Tissue Engineering"},{"key":"e_1_3_1_4_2","article-title":"Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method","author":"Moghadam A.A.A.","year":"2014","unstructured":"MoghadamA.A.A., HongW., KouzaniA., KaynakA., ZamaniR. and MontazamiR.Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method, Sensors and Actuators A: Physical (2014).","journal-title":"Sensors and Actuators A: Physical"},{"key":"e_1_3_1_5_2","article-title":"Experimental characterization of ionic polymer metal composite as a novel fractional order element","volume":"2013","author":"Caponetto R.","year":"2013","unstructured":"CaponettoR., GrazianiS., PappalardoF.L. and SapuppoF.Experimental characterization of ionic polymer metal composite as a novel fractional order element, Advances in Mathematical Physics2013 (2013).","journal-title":"Advances in Mathematical Physics"},{"key":"e_1_3_1_6_2","first-page":"905","article-title":"A Comparison between Robust and Parameterized Controllers for Fractional Order Modeled Ionic Polymeric Metal Composite Actuator","author":"Caponetto R.","year":"2013","unstructured":"CaponettoR., GrazianiS., PappalardoF.L. and XibiliaM.G.A Comparison between Robust and Parameterized Controllers for Fractional Order Modeled Ionic Polymeric Metal Composite Actuator, in Fractional Differentiation and its Applications (2013), pp. 905\u2013910.","journal-title":"Fractional Differentiation and its Applications"},{"key":"e_1_3_1_7_2","article-title":"Design of a robust quantitative feedback theory position controller for an ionic polymer metal composite actuator using an analytical dynamic model","author":"Moeinkhah H.","unstructured":"MoeinkhahH., AkbarzadehA. and RezaeepazhandJ.Design of a robust quantitative feedback theory position controller for an ionic polymer metal composite actuator using an analytical dynamic model, Journal of Intelligent Material Systems and Structures (1045), 1045389X13512906.","journal-title":"Journal of Intelligent Material Systems and Structures"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/22\/2\/025014"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sna.2010.03.007"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TMECH.2008.920021","article-title":"A control-oriented and physics-based model for ionic polymer\u2013metal composite actuators","volume":"13","author":"Chen Z.","year":"2008","unstructured":"ChenZ. and TanX.A control-oriented and physics-based model for ionic polymer\u2013metal composite actuators, Mechatronics, IEEE\/ASME Transactions on13 (2008), 519\u2013529.","journal-title":"Mechatronics, IEEE\/ASME Transactions on"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/14\/6\/051"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.1495888"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.372343"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/1\/1\/014"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/10\/4\/327"},{"key":"e_1_3_1_16_2","first-page":"691","article-title":"Characteristics and modeling of ICPF actuator","author":"Kanno R.","year":"1994","unstructured":"KannoR., KurataA., HattoriM., TadokoroS., TakamoriT. and OguroK.Characteristics and modeling of ICPF actuator, in Proceedings of the Japan-USA Symposium on Flexible Automation1994, pp. 691\u2013698.","journal-title":"Proceedings of the Japan-USA Symposium on Flexible Automation"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1177\/095965180421800601"},{"key":"e_1_3_1_18_2","first-page":"163","article-title":"Control of an inverted pendulum using an Ionic Polymer-Metal Composite actuator, in","author":"Hunt A.","year":"2010","unstructured":"HuntA., ChenZ., TanX. and KruusmaaM.Control of an inverted pendulum using an Ionic Polymer-Metal Composite actuator, in, Advanced Intelligent Mechatronics (AIM), 2010 IEEE\/ASME International Conference on (2010), pp. 163\u2013168.","journal-title":"Advanced Intelligent Mechatronics (AIM), 2010 IEEE\/ASME International Conference on"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2010.12.001"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1080\/19475411.2012.670141","article-title":"Wireless actuation and control of ionic polymer\u2013metal composite actuator using a microwave link","volume":"3","author":"Lee J.","year":"2012","unstructured":"LeeJ., YimW., BaeC. and KimK.Wireless actuation and control of ionic polymer\u2013metal composite actuator using a microwave link, International Journal of Smart and Nano Materials3 (2012), 244\u2013262.","journal-title":"International Journal of Smart and Nano Materials"},{"issue":"350","key":"e_1_3_1_21_2","article-title":"Modelling and fuzzy control of an efficient swimming ionic polymer-metal composite actuated robot","volume":"10","author":"Shen Q.","year":"2013","unstructured":"ShenQ., WangT., WenL. and LiangJ.Modelling and fuzzy control of an efficient swimming ionic polymer-metal composite actuated robot, Int J Adv Robot Syst10(350) (2013).","journal-title":"Int J Adv Robot Syst"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2011.2163524"},{"key":"e_1_3_1_23_2","first-page":"5073","article-title":"Adaptive Control for Ionic Polymer-Metal Composite Actuator Based on Continuous-Time Approach","author":"Chen X.","year":"2014","unstructured":"ChenX.Adaptive Control for Ionic Polymer-Metal Composite Actuator Based on Continuous-Time Approach, in World Congress (2014), pp. 5073\u20135078.","journal-title":"World Congress"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/978-3-319-05582-4_82","volume-title":"Robot Intelligence Technology and Applications 2","author":"Thinh N.T.","year":"2014","unstructured":"ThinhN.T., DungD.T.Adaptive Neuro-Fuzzy Control for Ionic Polymer Metal Composite Actuators, in Robot Intelligence Technology and Applications 2, ed: Springer (2014), pp. 939\u2013947."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1088\/0964-1726\/24\/4\/045040"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2014.2347356"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.4028\/www.scientific.net\/AMM.461.565","article-title":"A multi-step neural control for motor brain-machine interface by reinforcement learning","volume":"461","author":"Wang F.","year":"2014","unstructured":"WangF., XuK., ZhangQ.S., WangY.W. and ZhengX.X.A multi-step neural control for motor brain-machine interface by reinforcement learning, Applied Mechanics and Materials461 (2014), 565\u2013569.","journal-title":"Applied Mechanics and Materials"},{"key":"e_1_3_1_28_2","volume-title":"Reinforcement learning and approximate dynamic programming for feedback control","author":"Lewis F.L.","year":"2013","unstructured":"LewisF.L., LiuD., Reinforcement learning and approximate dynamic programming for feedback controlvol. 17, John Wiley & Sons, 2013."},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MCAS.2009.933854","article-title":"Reinforcement learning and adaptive dynamic programming for feedback control","volume":"9","author":"Lewis F.L.","year":"2009","unstructured":"LewisF.L. and VrabieD.Reinforcement learning and adaptive dynamic programming for feedback control, Circuits and Systems Magazine, IEEE9 (2009), 32\u201350.","journal-title":"Circuits and Systems Magazine, IEEE"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TSMCB.2008.922018","article-title":"Improved adaptive-reinforcement learning control for morphing unmanned air vehicles","volume":"38","author":"Valasek J.","year":"2008","unstructured":"ValasekJ., DoebblerJ., TandaleM.D. and MeadeA.J.Improved adaptive-reinforcement learning control for morphing unmanned air vehicles, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics38 (2008), 1014\u20131020.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"174","DOI":"10.2514\/1.11388","article-title":"A reinforcement learning - Adaptive control architecture for morphing","author":"Valasek J.","year":"2005","unstructured":"ValasekJ., TandaleM.D. and RongJ.A reinforcement learning - Adaptive control architecture for morphing, Journal of Aerospace Computing, Information and Communication (2005), 174\u2013195.","journal-title":"Journal of Aerospace Computing, Information and Communication"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/37.126844","article-title":"Reinforcement learning is direct adaptive optimal control","volume":"12","author":"Sutton R.S.","year":"1992","unstructured":"SuttonR.S., BartoA.G. and WilliamsR.J.Reinforcement learning is direct adaptive optimal control, Control Systems, IEEE12 (1992), 19\u201322.","journal-title":"Control Systems, IEEE"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/0888-613X(92)90020-Z","article-title":"A reinforcement learning\u2014 based architecture for fuzzy logic control","volume":"6","author":"Berenji H.R.","year":"1992","unstructured":"BerenjiH.R.A reinforcement learning\u2014 based architecture for fuzzy logic control, International Journal of Approximate Reasoning6 (1992), 267\u2013292.","journal-title":"International Journal of Approximate Reasoning"},{"key":"e_1_3_1_34_2","volume-title":"Reinforcement learning: An introduction","author":"Sutton R.S.","year":"1998","unstructured":"SuttonR.S., BartoA.G.Reinforcement learning: An introduction, vol. 1: Cambridge Univ Press, 1998."},{"key":"e_1_3_1_35_2","first-page":"1038","article-title":"Generalization in reinforcement learning: Successful examples using sparse coarse coding","author":"Sutton R.S.","year":"1996","unstructured":"SuttonR.S.Generalization in reinforcement learning: Successful examples using sparse coarse coding, Advances in Neural Information Processing Systems, 1996, pp. 1038\u20131044.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-2012-0558"},{"key":"e_1_3_1_37_2","first-page":"123","article-title":"Experiments on the use of option policies in reinforcement learning","volume":"13","author":"Friske L.M.","year":"2002","unstructured":"FriskeL.M. and RibeiroC.H.Experiments on the use of option policies in reinforcement learning, Journal of Intelligent & Fuzzy Systems13 (2002), 123\u2013132.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"e_1_3_1_39_2","unstructured":"WatkinsC.J.C.H.Learning from delayed rewards University of Cambridge 1989."},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1109\/72.159061","article-title":"Learning and tuning fuzzy logic controllers through reinforcements","volume":"3","author":"Berenji H.R.","year":"1992","unstructured":"BerenjiH.R. and KhedkarP.Learning and tuning fuzzy logic controllers through reinforcements, Neural Networks, IEEE Transactions on3 (1992), 724\u2013740.","journal-title":"Neural Networks, IEEE Transactions on"},{"key":"e_1_3_1_41_2","first-page":"2014","article-title":"Yaw Moment Control Using Fuzzy Reinforcement Learning","author":"Akbari A.A.","unstructured":"AkbariA.A. and GoharimaneshM., Yaw Moment Control Using Fuzzy Reinforcement Learning, in Advanced Vehicle Control (AVEC14), 2014.","journal-title":"Advanced Vehicle Control (AVEC14)"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.301"},{"key":"e_1_3_1_43_2","first-page":"1","article-title":"Combining the principles of fuzzy logic and reinforcement learning for control of dynamic systems","volume":"27","author":"Goharimanesh M.","year":"2015","unstructured":"GoharimaneshM., AkbariA.A. and Naghibi-SistaniM.-B.Combining the principles of fuzzy logic and reinforcement learning for control of dynamic systems, Journal of Applied and Computational Sciences in Mechanics27 (2015), 1\u201314.","journal-title":"Journal of Applied and Computational Sciences in Mechanics"},{"key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1109\/FUZZY.1996.553542","article-title":"Fuzzy Q-learning for generalization of reinforcement learning","author":"Berenji H.R.","year":"1996","unstructured":"BerenjiH.R.Fuzzy Q-learning for generalization of reinforcement learning, in Fuzzy Systems, 1996, Proceedings of the Fifth IEEE International Conference on, 1996, pp. 2208\u20132214.","journal-title":"Fuzzy Systems, 1996, Proceedings of the Fifth IEEE International Conference on"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.fss.2008.11.026","article-title":"Reinforcement distribution in fuzzy Q-learning","volume":"160","author":"Bonarini A.","year":"2009","unstructured":"BonariniA., LazaricA., MontroneF. and RestelliM.Reinforcement distribution in fuzzy Q-learning, Fuzzy Sets and Systems160 (2009), 1420\u20131443.","journal-title":"Fuzzy Sets and Systems"},{"key":"e_1_3_1_46_2","first-page":"133","article-title":"Diabetic control using genetic fuzzy-PI controller","volume":"16","author":"Goharimanesh M.","year":"2014","unstructured":"GoharimaneshM., LashkaripourA., ShariatniaS. and AkbariA.Diabetic control using genetic fuzzy-PI controller, International Journal of Fuzzy Systems16 (2014), 133.","journal-title":"International Journal of Fuzzy Systems"},{"key":"e_1_3_1_47_2","volume-title":"Introduction to Quality Engineering: Designing Quality into Products and Processes","author":"Taguchi G.","year":"1986","unstructured":"TaguchiG.Introduction to Quality Engineering: Designing Quality into Products and Processes. Tokyo: The Organization, 1986."},{"key":"e_1_3_1_48_2","volume-title":"System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs","author":"Taguchi G.","year":"1987","unstructured":"TaguchiG., TungL.W., ClausingD., System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. vol. 2: UNIPUB\/Kraus International Publications, 1987."},{"key":"e_1_3_1_49_2","volume-title":"Quality Engineering in Production Systems","author":"Taguchi G.","year":"1989","unstructured":"TaguchiG., ElsayedE.A., HsiangT.C., Quality Engineering in Production Systems. McGraw-Hill College, 1989."},{"key":"e_1_3_1_50_2","volume-title":"Taguchi Methods","author":"Taguchi G.I.","year":"1994","unstructured":"TaguchiG.I., YokoyamaY., Taguchi Methodsvol. 2: ASI, 1994."},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40092-014-0061-y","article-title":"More efficiency in fuel consumption using gearbox optimization based on Taguchi method","volume":"10","author":"Goharimanesh M.","year":"2014","unstructured":"GoharimaneshM., AkbariA. and TootoonchiA.A.More efficiency in fuel consumption using gearbox optimization based on Taguchi method, Journal of Industrial Engineering International10 (2014), 1\u20138.","journal-title":"Journal of Industrial Engineering International"},{"key":"e_1_3_1_52_2","first-page":"233","article-title":"Optimum parameters of nonlinear integrator using design of experiments based on Taguchi method","volume":"46","author":"Goharimanesh M.","year":"2015","unstructured":"GoharimaneshM. and AkbariA.Optimum parameters of nonlinear integrator using design of experiments based on Taguchi method, Journal of Applied Mechanics46 (2015), 233\u2013241.","journal-title":"Journal of Applied Mechanics"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-161211","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-161211","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-161211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:49Z","timestamp":1777455589000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-161211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,15]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,7]]}},"alternative-id":["10.3233\/JIFS-161211"],"URL":"https:\/\/doi.org\/10.3233\/jifs-161211","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,15]]}}}