{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:31:55Z","timestamp":1762014715171,"version":"build-2065373602"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007161","name":"Secretar\u00eda de Investigaci\u00f3n y Posgrado, Instituto Polit\u00e9cnico Nacional","doi-asserted-by":"publisher","award":["20241335"],"award-info":[{"award-number":["20241335"]}],"id":[{"id":"10.13039\/501100007161","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00521-025-11050-7","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T13:29:31Z","timestamp":1743946171000},"page":"27781-27810","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Micro-bio-inspired metaheuristics for optimized adaptive controller tuning: enhancing BLDC motor performance"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0942-7417","authenticated-orcid":false,"given":"Alam Gabriel","family":"Rojas-L\u00f3pez","sequence":"first","affiliation":[]},{"given":"Miguel Gabriel","family":"Villarreal-Cervantes","sequence":"additional","affiliation":[]},{"given":"Alejandro","family":"Rodr\u00edguez-Molina","sequence":"additional","affiliation":[]},{"given":"Irving","family":"Luna-Ortiz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,6]]},"reference":[{"key":"11050_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2020.106415","volume":"109","author":"P Tang","year":"2021","unstructured":"Tang P, Zhang F, Ye J, Lin D (2021) An integral tsmc-based adaptive fault-tolerant control for quadrotor with external disturbances and parametric uncertainties. Aerosp Sci Technol 109:106415. https:\/\/doi.org\/10.1016\/j.ast.2020.106415","journal-title":"Aerosp Sci Technol"},{"key":"11050_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.isatra.2020.11.026","volume":"112","author":"MS Mahmoud","year":"2021","unstructured":"Mahmoud MS, Maaruf M, El-Ferik S (2021) Neuro-adaptive output feedback control of the continuous polymerization reactor subjected to parametric uncertainties and external disturbances. ISA Trans 112:1\u201311. https:\/\/doi.org\/10.1016\/j.isatra.2020.11.026","journal-title":"ISA Trans"},{"key":"11050_CR3","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.isatra.2022.06.044","volume":"133","author":"S Li","year":"2023","unstructured":"Li S, Yan Y, Jiang D, Guo Q (2023) Synchronized control of multiple electrohydraulic systems with terminal sliding mode observer under parametric uncertainty and external load. ISA Trans 133:475\u2013484. https:\/\/doi.org\/10.1016\/j.isatra.2022.06.044","journal-title":"ISA Trans"},{"issue":"18","key":"11050_CR4","doi-asserted-by":"publisher","first-page":"14584","DOI":"10.1016\/j.jfranklin.2023.11.023","volume":"360","author":"SR Nekoo","year":"2023","unstructured":"Nekoo SR, Ollero A (2023) A robust state-dependent riccati equation controller with parameter uncertainty and matched disturbance. J Franklin Inst 360(18):14584\u201314595. https:\/\/doi.org\/10.1016\/j.jfranklin.2023.11.023","journal-title":"J Franklin Inst"},{"key":"11050_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106342","volume":"93","author":"A Rodr\u00edguez-Molina","year":"2020","unstructured":"Rodr\u00edguez-Molina A, Mezura-Montes E, Villarreal-Cervantes MG, Aldape-P\u00e9rez M (2020) Multi-objective meta-heuristic optimization in intelligent control: a survey on the controller tuning problem. Appl Soft Comput 93:106342. https:\/\/doi.org\/10.1016\/j.asoc.2020.106342","journal-title":"Appl Soft Comput"},{"key":"11050_CR6","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.isatra.2022.08.007","volume":"134","author":"F Alyoussef","year":"2023","unstructured":"Alyoussef F, Kaya I (2023) Simple pi-pd tuning rules based on the centroid of the stability region for controlling unstable and integrating processes. ISA Trans 134:238\u2013255. https:\/\/doi.org\/10.1016\/j.isatra.2022.08.007","journal-title":"ISA Trans"},{"key":"11050_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2021.100054","volume":"16","author":"T Anitha","year":"2021","unstructured":"Anitha T, Gopu G (2021) Controlled mechanical ventilation for enhanced measurement in pressure and flow sensors. Meas: Sens 16:100054. https:\/\/doi.org\/10.1016\/j.measen.2021.100054","journal-title":"Meas: Sens"},{"key":"11050_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-62554-2_13","author":"AG Rojas-L\u00f3pez","year":"2020","unstructured":"Rojas-L\u00f3pez AG, Villarreal-Cervantes MG, Rodr\u00edguez-Molina A, Garc\u00eda-Mendoza CV (2020) Offline optimum tuning of the proportional integral controller for speed regulation of a bldc motor through bio-inspired algorithms. Int Congr Telemat Comput. https:\/\/doi.org\/10.1007\/978-3-030-62554-2_13. (Springer)","journal-title":"Int Congr Telemat Comput"},{"key":"11050_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108338","volume":"133","author":"S-Y Paek","year":"2024","unstructured":"Paek S-Y, Kong Y-S, Pak S-H, Kang J-S, Yun J-N, Kil H-I, Hwang C-J (2024) Robust optimal tuning of a reduced active disturbance rejection controller based on first order plus dead time model approximation. Eng Appl Artif Intell 133:108338. https:\/\/doi.org\/10.1016\/j.engappai.2024.108338","journal-title":"Eng Appl Artif Intell"},{"key":"11050_CR10","doi-asserted-by":"publisher","unstructured":"Landau ID, Lozano R, M\u2019Saad M, Karimi A (2011) Adaptive Control: Algorithms, Analysis and Applications. Springer, London. https:\/\/doi.org\/10.1007\/978-0-85729-664-1","DOI":"10.1007\/978-0-85729-664-1"},{"key":"11050_CR11","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.isatra.2022.04.001","volume":"130","author":"N Zhu","year":"2022","unstructured":"Zhu N, Gao X-T, Huang C-Q (2022) A data-driven approach for on-line auto-tuning of minimum variance pid controller. ISA Trans 130:325\u2013342. https:\/\/doi.org\/10.1016\/j.isatra.2022.04.001","journal-title":"ISA Trans"},{"key":"11050_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2022.101097","volume":"35","author":"N Aliman","year":"2022","unstructured":"Aliman N, Ramli R, Mohamed Haris S, Soleimani Amiri M, Van M (2022) A robust adaptive-fuzzy-proportional-derivative controller for a rehabilitation lower limb exoskeleton. Eng Sci Technol, Int J 35:101097. https:\/\/doi.org\/10.1016\/j.jestch.2022.101097","journal-title":"Eng Sci Technol, Int J"},{"key":"11050_CR13","doi-asserted-by":"publisher","unstructured":"Rojas-L\u00f3pez AG, Villarreal-Cervantes MG, Rodr\u00edguez-Molina A (2023) Optimum online controller tuning through de and pso algorithms: comparative study with bldc motor and offline controller tuning strategy. In: 2023 10th international conference on soft computing & machine intelligence (ISCMI), pp. 215\u2013221. IEEE, Mexico City, Mexico. https:\/\/doi.org\/10.1109\/iscmi59957.2023.10458633","DOI":"10.1109\/iscmi59957.2023.10458633"},{"key":"11050_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/math10121977","author":"A Rodr\u00edguez-Molina","year":"2022","unstructured":"Rodr\u00edguez-Molina A, Villarreal-Cervantes MG, Serrano-P\u00e9rez O, Sol\u00eds-Romero J, Silva-Ortigoza R (2022) Optimal tuning of the speed control for brushless dc motor based on chaotic online differential evolution. Mathematics. https:\/\/doi.org\/10.3390\/math10121977","journal-title":"Mathematics"},{"key":"11050_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.prime.2023.100280","volume":"5","author":"MW Hasan","year":"2023","unstructured":"Hasan MW (2023) Disturbance rejection controller design based on adaptive nonlinear fopid controller and chaotic woa with a neuro-fuzzy approximation for urv robot. e-Prime-Adv Electr Eng Electron Energy 5:100280. https:\/\/doi.org\/10.1016\/j.prime.2023.100280","journal-title":"e-Prime-Adv Electr Eng Electron Energy"},{"key":"11050_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2023.105500","volume":"135","author":"GV Hollweg","year":"2023","unstructured":"Hollweg GV, Oliveira Evald PJD, Mattos E, Borin LC, Tambara RV, Montagner VF (2023) Self-tuning methodology for adaptive controllers based on genetic algorithms applied for grid-tied power converters. Control Eng Pract 135:105500. https:\/\/doi.org\/10.1016\/j.conengprac.2023.105500","journal-title":"Control Eng Pract"},{"key":"11050_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-78841-6","volume-title":"Model Order Reduction: Theory, Research Aspects and Applications","author":"WH Schilders","year":"2008","unstructured":"Schilders WH, Vorst HA, Rommes J (2008) Model Order Reduction: Theory, Research Aspects and Applications, vol 13. Springer, Berlin, Heidelberg"},{"issue":"4","key":"11050_CR18","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s11831-014-9111-2","volume":"21","author":"U Baur","year":"2014","unstructured":"Baur U, Benner P, Feng L (2014) Model order reduction for linear and nonlinear systems: a system-theoretic perspective. Arch Comput Methods Eng 21(4):331\u2013358. https:\/\/doi.org\/10.1007\/s11831-014-9111-2","journal-title":"Arch Comput Methods Eng"},{"key":"11050_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2020.104488","volume":"101","author":"L Roveda","year":"2020","unstructured":"Roveda L, Forgione M, Piga D (2020) Robot control parameters auto-tuning in trajectory tracking applications. Control Eng Pract 101:104488. https:\/\/doi.org\/10.1016\/j.conengprac.2020.104488","journal-title":"Control Eng Pract"},{"issue":"2","key":"11050_CR20","doi-asserted-by":"publisher","first-page":"8724","DOI":"10.1016\/j.ifacol.2020.12.276","volume":"53","author":"L Roveda","year":"2020","unstructured":"Roveda L, Forgione M, Piga D (2020) Two-stage robot controller auto-tuning methodology for trajectory tracking applications. IFAC-PapersOnLine 53(2):8724\u20138731. https:\/\/doi.org\/10.1016\/j.ifacol.2020.12.276","journal-title":"IFAC-PapersOnLine"},{"key":"11050_CR21","doi-asserted-by":"publisher","unstructured":"Khowaja K, Shcherbatyy M, Karl\u00a0H\u00e4rdle W (2023) Surrogate models for optimization of dynamical systems, pp. 563\u2013593. Springer, Moscow, Russia. https:\/\/doi.org\/10.1007\/978-3-031-30114-8_16","DOI":"10.1007\/978-3-031-30114-8_16"},{"key":"11050_CR22","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1016\/j.asoc.2016.09.042","volume":"52","author":"H Salehinejad","year":"2017","unstructured":"Salehinejad H, Rahnamayan S, Tizhoosh HR (2017) Micro-differential evolution: Diversity enhancement and a comparative study. Appl Soft Comput 52:812\u2013833. https:\/\/doi.org\/10.1016\/j.asoc.2016.09.042","journal-title":"Appl Soft Comput"},{"key":"11050_CR23","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.swevo.2018.04.011","volume":"44","author":"H Ma","year":"2019","unstructured":"Ma H, Shen S, Yu M, Yang Z, Fei M, Zhou H (2019) Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey. Swarm Evol Comput 44:365\u2013387. https:\/\/doi.org\/10.1016\/j.swevo.2018.04.011","journal-title":"Swarm Evol Comput"},{"key":"11050_CR24","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.swevo.2018.08.015","volume":"44","author":"G Wu","year":"2019","unstructured":"Wu G, Mallipeddi R, Suganthan PN (2019) Ensemble strategies for population-based optimization algorithms - a survey. Swarm Evol Comput 44:695\u2013711. https:\/\/doi.org\/10.1016\/j.swevo.2018.08.015","journal-title":"Swarm Evol Comput"},{"key":"11050_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100844","volume":"62","author":"Y Wu","year":"2021","unstructured":"Wu Y (2021) A survey on population-based meta-heuristic algorithms for motion planning of aircraft. Swarm Evol Comput 62:100844. https:\/\/doi.org\/10.1016\/j.swevo.2021.100844","journal-title":"Swarm Evol Comput"},{"key":"11050_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126630","volume":"554","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Shi Y, Wei Y, Luo Q, Tang Z (2023) Nature-inspired algorithms for 0\u20131 knapsack problem: A survey. Neurocomputing 554:126630. https:\/\/doi.org\/10.1016\/j.neucom.2023.126630","journal-title":"Neurocomputing"},{"key":"11050_CR27","unstructured":"Goldberg DE (1989) Sizing populations for serial and parallel genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 70\u201379. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA"},{"key":"11050_CR28","doi-asserted-by":"publisher","unstructured":"Dasgupta S, Biswas A, Das S, Panigrahi BK, Abraham A (2009) A micro-bacterial foraging algorithm for high-dimensional optimization. In: 2009 IEEE congress on evolutionary computation. IEEE, Trondheim, Norway. https:\/\/doi.org\/10.1109\/cec.2009.4983025","DOI":"10.1109\/cec.2009.4983025"},{"key":"11050_CR29","doi-asserted-by":"publisher","DOI":"10.1115\/1.403740","author":"AP Deshmukh","year":"2017","unstructured":"Deshmukh AP, Allison JT (2017) Design of dynamic systems using surrogate models of derivative functions. Journal of Mechanical Design. https:\/\/doi.org\/10.1115\/1.403740","journal-title":"Journal of Mechanical Design"},{"key":"11050_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123070","volume":"245","author":"AG Rojas-L\u00f3pez","year":"2024","unstructured":"Rojas-L\u00f3pez AG, Villarreal-Cervantes MG, Rodr\u00edguez-Molina A (2024) Surrogate indirect adaptive controller tuning based on polynomial response surface method and bioinspired optimization: Application to the brushless direct current motor controller. Expert Syst Appl 245:123070. https:\/\/doi.org\/10.1016\/j.eswa.2023.123070","journal-title":"Expert Syst Appl"},{"key":"11050_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2019.116288","volume":"259","author":"Z Wu","year":"2020","unstructured":"Wu Z, Han Z (2020) Micro-ga optimization analysis of the effect of diesel injection strategy on natural gas-diesel dual-fuel combustion. Fuel 259:116288. https:\/\/doi.org\/10.1016\/j.fuel.2019.116288","journal-title":"Fuel"},{"key":"11050_CR32","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.egyr.2019.11.095","volume":"6","author":"E Rosado-Tamariz","year":"2020","unstructured":"Rosado-Tamariz E, Zuniga-Garcia MA, Batres R (2020) Optimization of a drum boiler startup using dynamic simulation and a micro-genetic algorithm. Energy Rep 6:410\u2013416. https:\/\/doi.org\/10.1016\/j.egyr.2019.11.095","journal-title":"Energy Rep"},{"key":"11050_CR33","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.optcom.2012.11.017","volume":"293","author":"Y-S Kim","year":"2013","unstructured":"Kim Y-S, Choi A-S, Jeong J-W (2013) Applying micro genetic algorithm to numerical model for luminous intensity distribution of planar prism led luminaire. Opt Commun 293:22\u201330. https:\/\/doi.org\/10.1016\/j.optcom.2012.11.017","journal-title":"Opt Commun"},{"key":"11050_CR34","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.compchemeng.2013.04.016","volume":"57","author":"R Batres","year":"2013","unstructured":"Batres R (2013) Generation of operating procedures for a mixing tank with a micro genetic algorithm. Comput & Chem Eng 57:112\u2013121. https:\/\/doi.org\/10.1016\/j.compchemeng.2013.04.016","journal-title":"Comput & Chem Eng"},{"key":"11050_CR35","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.jprocont.2017.02.011","volume":"53","author":"K Vinther","year":"2017","unstructured":"Vinther K, Nielsen RJ, Andersen P, Bendtsen JD (2017) Optimization of interconnected absorption cycle heat pumps with micro-genetic algorithms. J Process Control 53:26\u201336. https:\/\/doi.org\/10.1016\/j.jprocont.2017.02.011","journal-title":"J Process Control"},{"key":"11050_CR36","doi-asserted-by":"publisher","unstructured":"Batres R (2012) Generating operating procedures using a micro genetic algorithm, pp. 1316\u20131320. Elsevier, Singapore. https:\/\/doi.org\/10.1016\/b978-0-444-59506-5.50094-8","DOI":"10.1016\/b978-0-444-59506-5.50094-8"},{"issue":"5","key":"11050_CR37","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/s0168-874x(00)00052-4","volume":"37","author":"BH Dennis","year":"2001","unstructured":"Dennis BH, Dulikravich GS (2001) Optimization of magneto-hydrodynamic control of diffuser flows using micro-genetic algorithms and least-squares finite elements. Finite Elem Anal Des 37(5):349\u2013363. https:\/\/doi.org\/10.1016\/s0168-874x(00)00052-4","journal-title":"Finite Elem Anal Des"},{"issue":"9","key":"11050_CR38","doi-asserted-by":"publisher","first-page":"3839","DOI":"10.1016\/j.asoc.2013.05.005","volume":"13","author":"VH Hinojosa","year":"2013","unstructured":"Hinojosa VH, Araya R (2013) Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems. Appl Soft Comput 13(9):3839\u20133852. https:\/\/doi.org\/10.1016\/j.asoc.2013.05.005","journal-title":"Appl Soft Comput"},{"key":"11050_CR39","doi-asserted-by":"publisher","unstructured":"Guan D, Cai Z, Kong Z (2009) Reactive power and voltage control using micro-genetic algorithm. In: 2009 International Conference on Mechatronics and Automation. IEEE, Changchun, China. https:\/\/doi.org\/10.1109\/icma.2009.5246087","DOI":"10.1109\/icma.2009.5246087"},{"issue":"08","key":"11050_CR40","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.1109\/tla.2019.8932332","volume":"17","author":"FA Ch\u00e1vez Estrada","year":"2019","unstructured":"Ch\u00e1vez Estrada FA, Herrera Lozada JC, Sandoval Guti\u00e9rrez J, Cervantes Valencia MI (2019) Performance between algorithm and micro genetic algorithm to solve the robot locomotion. IEEE Lat Am Trans 17(08):1244\u20131251. https:\/\/doi.org\/10.1109\/tla.2019.8932332","journal-title":"IEEE Lat Am Trans"},{"key":"11050_CR41","doi-asserted-by":"publisher","unstructured":"Bakare GA, Venayagamoorthy GK, Aliyu UO (2005) Reactive power and voltage control of the nigerian grid system using micro-genetic algorithm. In: IEEE power engineering society general meeting, 2005. IEEE, San Francisco, CA, USA. https:\/\/doi.org\/10.1109\/pes.2005.1489424","DOI":"10.1109\/pes.2005.1489424"},{"key":"11050_CR42","doi-asserted-by":"publisher","unstructured":"Jerabek V, Lachiver G (1995) Micro-genetic algorithms in the optimisation of neuro-fuzzy controllers. In: Proceedings 1995 canadian conference on electrical and computer engineering. CCECE-95. IEEE, Montreal, QC, Canada. https:\/\/doi.org\/10.1109\/ccece.1995.528086","DOI":"10.1109\/ccece.1995.528086"},{"key":"11050_CR43","doi-asserted-by":"publisher","unstructured":"Li Z, Yang Y, Gong X, Lin Y, Liu G (2008) Fuzzy control of the semi-active suspension with mr damper based on $$\\mu$$ga. In: 2008 IEEE Vehicle Power and Propulsion Conference. IEEE, Harbin, China. https:\/\/doi.org\/10.1109\/vppc.2008.4677414","DOI":"10.1109\/vppc.2008.4677414"},{"key":"11050_CR44","doi-asserted-by":"publisher","unstructured":"Ismail O, Bedwani W (2001) Compliant motion control using variable structure pid control system. In: Proceeding of the 2001 IEEE international symposium on intelligent control (ISIC \u201901) (Cat. No.01CH37206). ISIC-01. IEEE, Mexico City, Mexico. https:\/\/doi.org\/10.1109\/isic.2001.971542","DOI":"10.1109\/isic.2001.971542"},{"key":"11050_CR45","doi-asserted-by":"publisher","unstructured":"Hinojosa J, Domenech-Asensi G (2007) Multiple adaptive neuro-fuzzy inference systems for accurate microwave cad applications. In: 2007 18th european conference on circuit theory and design. IEEE, Seville, Spain. https:\/\/doi.org\/10.1109\/ecctd.2007.4529709","DOI":"10.1109\/ecctd.2007.4529709"},{"key":"11050_CR46","doi-asserted-by":"publisher","unstructured":"Yao L, Jiang J-N (2011) Design of adaptive fuzzy controller with observer using modulated membership functions. In: Proceedings of 2011 international conference on modelling, identification and control. IEEE, Shanghai, China. https:\/\/doi.org\/10.1109\/icmic.2011.5973759","DOI":"10.1109\/icmic.2011.5973759"},{"key":"11050_CR47","doi-asserted-by":"publisher","unstructured":"Reddy BBK, Homaifar A, Esterline AC (2006) Velocity control of electric propulsion space vehicles using heliocentric gravitational sling. In: 2006 world automation congress. IEEE, Budapest, Hungary. https:\/\/doi.org\/10.1109\/wac.2006.376065","DOI":"10.1109\/wac.2006.376065"},{"key":"11050_CR48","doi-asserted-by":"publisher","unstructured":"Kumar\u00a0Reddy BB, Esterline AC, Homaifar A (2006) Minimal fuel consumption of electric propulsion space vehicles for deep space exploration. In: 2006 IEEE aerospace conference. IEEE, Big Sky, MT, USA. https:\/\/doi.org\/10.1109\/aero.2006.1655989","DOI":"10.1109\/aero.2006.1655989"},{"key":"11050_CR49","doi-asserted-by":"publisher","unstructured":"Yao L, Pan W-J (2009) Fuzzy adaptive controller with modulated membership function for a mimo uncertain nonlinear system. In: 2009 fourth international conference on innovative computing, information and control (ICICIC). IEEE, Kaohsiung, Taiwan. https:\/\/doi.org\/10.1109\/icicic.2009.218","DOI":"10.1109\/icicic.2009.218"},{"key":"11050_CR50","doi-asserted-by":"publisher","unstructured":"Pan W-J, Lin C-C, Yao L (2009) Adaptive fuzzy control with modulated membership function applies to path tracking based on location system. In: 2009 Eighth IEEE international conference on dependable, autonomic and secure computing. IEEE, Chengdu, China. https:\/\/doi.org\/10.1109\/dasc.2009.135","DOI":"10.1109\/dasc.2009.135"},{"key":"11050_CR51","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.jmsy.2023.01.003","volume":"67","author":"L Roveda","year":"2023","unstructured":"Roveda L, Veerappan P, Maccarini M, Bucca G, Ajoudani A, Piga D (2023) A human-centric framework for robotic task learning and optimization. J Manuf Syst 67:68\u201379. https:\/\/doi.org\/10.1016\/j.jmsy.2023.01.003","journal-title":"J Manuf Syst"},{"key":"11050_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.111116","volume":"151","author":"A Rodr\u00edguez-Molina","year":"2024","unstructured":"Rodr\u00edguez-Molina A, Villarreal-Cervantes MG, Pantoja-Garc\u00eda JS, Rojas-L\u00f3pez AG, Hern\u00e1ndez-Castillo E, Mej\u00eda-Rodr\u00edguez R (2024) Metaheuristic adaptive control based on polynomial regression and differential evolution for robotic manipulators. Appl Soft Comput 151:111116. https:\/\/doi.org\/10.1016\/j.asoc.2023.111116","journal-title":"Appl Soft Comput"},{"key":"11050_CR53","doi-asserted-by":"publisher","unstructured":"Rojas-L\u00f3pez AG, Villarreal-Cervantes MG, Rodr\u00edguez-Molina A, Paredes-Ballesteros JA (2024) Surrogate adaptive controller tuning based on de in a 3r serial robot: a comparative analysis. In: 2024 IEEE 19th conference on industrial electronics and applications (ICIEA), vol. 275, pp. 1\u20137. IEEE, Norway. https:\/\/doi.org\/10.1109\/iciea61579.2024.10664799","DOI":"10.1109\/iciea61579.2024.10664799"},{"key":"11050_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.122493","volume":"358","author":"W Xu","year":"2024","unstructured":"Xu W, Svetozarevic B, Di Natale L, Heer P, Jones CN (2024) Data-driven adaptive building thermal controller tuning with constraints: a primal-dual contextual bayesian optimization approach. Appl Energy 358:122493. https:\/\/doi.org\/10.1016\/j.apenergy.2023.122493","journal-title":"Appl Energy"},{"key":"11050_CR55","doi-asserted-by":"publisher","unstructured":"Rojas-L\u00f3pez AG, Villarreal-Cervantes MG, Rodr\u00edguez-Molina A, Paredes-Ballesteros JA (2024) Comparative analysis of indirect adaptive controller tuning strategies using surrogate and model-based techniques applied to the omnidirectional mobile robot. In: 2024 10th international conference on control, decision and information technologies (CoDIT), vol. 275, pp. 91\u201396. IEEE, Malta. https:\/\/doi.org\/10.1109\/codit62066.2024.10708577","DOI":"10.1109\/codit62066.2024.10708577"},{"issue":"1","key":"11050_CR56","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MIE.2020.3008136","volume":"15","author":"A Grau","year":"2021","unstructured":"Grau A, Indri M, Lo Bello L, Sauter T (2021) Robots in industry: the past, present, and future of a growing collaboration with humans. IEEE Ind Electron Mag 15(1):50\u201361. https:\/\/doi.org\/10.1109\/MIE.2020.3008136","journal-title":"IEEE Ind Electron Mag"},{"key":"11050_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2020.102384","volume":"69","author":"A Rodr\u00edguez-Molina","year":"2020","unstructured":"Rodr\u00edguez-Molina A, Villarreal-Cervantes MG, Aldape-P\u00e9rez M (2020) Indirect adaptive control using the novel online hypervolume-based differential evolution for the four-bar mechanism. Mechatronics 69:102384. https:\/\/doi.org\/10.1016\/j.mechatronics.2020.102384","journal-title":"Mechatronics"},{"key":"11050_CR58","doi-asserted-by":"publisher","first-page":"54833","DOI":"10.1109\/access.2022.3175011","volume":"10","author":"D Mohanraj","year":"2022","unstructured":"Mohanraj D, Aruldavid R, Verma R, Sathiyasekar K, Barnawi AB, Chokkalingam B, Mihet-Popa L (2022) A review of bldc motor: state of art, advanced control techniques, and applications. IEEE Access 10:54833\u201354869. https:\/\/doi.org\/10.1109\/access.2022.3175011","journal-title":"IEEE Access"},{"key":"11050_CR59","doi-asserted-by":"publisher","unstructured":"Sakunthala S, Kiranmayi R, Mandadi PN (2017) A study on industrial motor drives: comparison and applications of pmsm and bldc motor drives. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS), pp. 537\u2013540. https:\/\/doi.org\/10.1109\/ICECDS.2017.8390224","DOI":"10.1109\/ICECDS.2017.8390224"},{"key":"11050_CR60","doi-asserted-by":"crossref","unstructured":"Xia C-L (2012) Permanent magnet brushless DC motor drives and controls. John Wiley & Sons","DOI":"10.1002\/9781118188347"},{"key":"11050_CR61","doi-asserted-by":"publisher","first-page":"20393","DOI":"10.1109\/ACCESS.2017.2757959","volume":"5","author":"MG Villarreal-Cervantes","year":"2017","unstructured":"Villarreal-Cervantes MG, Rodr\u00edguez-Molina A, Garc\u00eda-Mendoza C-V, Pe\u00f1aloza-Mej\u00eda O, Sep\u00falveda-Cervantes G (2017) Multi-objective on-line optimization approach for the dc motor controller tuning using differential evolution. IEEE Access 5:20393\u201320407. https:\/\/doi.org\/10.1109\/ACCESS.2017.2757959","journal-title":"IEEE Access"},{"key":"11050_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2023.113688","volume":"301","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Zhou X, Du H, Cui P (2023) A new model predictive control approach integrating physical and data-driven modelling for improved energy performance of district heating substations. Energy and Buildings 301:113688. https:\/\/doi.org\/10.1016\/j.enbuild.2023.113688","journal-title":"Energy and Buildings"},{"key":"11050_CR63","doi-asserted-by":"publisher","unstructured":"Khandelwal MK, Sharma N (2023) A survey on particle swarm optimization algorithm, pp. 591\u2013602. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-3485-0_47","DOI":"10.1007\/978-981-99-3485-0_47"},{"issue":"1","key":"11050_CR64","doi-asserted-by":"publisher","first-page":"157","DOI":"10.3390\/make1010010","volume":"1","author":"S Sengupta","year":"2018","unstructured":"Sengupta S, Basak S, Peters R (2018) Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction 1(1):157\u2013191. https:\/\/doi.org\/10.3390\/make1010010","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"11050_CR65","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.swevo.2018.06.010","volume":"44","author":"KR Opara","year":"2019","unstructured":"Opara KR, Arabas J (2019) Differential evolution: a survey of theoretical analyses. Swarm Evol Comput 44:546\u2013558. https:\/\/doi.org\/10.1016\/j.swevo.2018.06.010","journal-title":"Swarm Evol Comput"},{"issue":"5","key":"11050_CR66","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1007\/s00158-005-0527-z","volume":"30","author":"CA Coello Coello","year":"2005","unstructured":"Coello Coello CA, Pulido GT (2005) Multiobjective structural optimization using a microgenetic algorithm. Struct Multidiscip Optim 30(5):388\u2013403. https:\/\/doi.org\/10.1007\/s00158-005-0527-z","journal-title":"Struct Multidiscip Optim"},{"key":"11050_CR67","doi-asserted-by":"publisher","DOI":"10.5897\/ijps11.303","author":"F Viveros-Jim\u00e9nez","year":"2012","unstructured":"Viveros-Jim\u00e9nez F (2012) Empirical analysis of a micro-evolutionary algorithm for numerical optimization. International Journal of the Physical Sciences. https:\/\/doi.org\/10.5897\/ijps11.303","journal-title":"International Journal of the Physical Sciences"},{"issue":"2","key":"11050_CR68","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TEVC.2019.2921598","volume":"24","author":"C Huang","year":"2020","unstructured":"Huang C, Li Y, Yao X (2020) A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans Evol Comput 24(2):201\u2013216. https:\/\/doi.org\/10.1109\/TEVC.2019.2921598","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"11050_CR69","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac J, Garc\u00eda S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3\u201318. https:\/\/doi.org\/10.1016\/j.swevo.2011.02.002","journal-title":"Swarm Evol Comput"},{"key":"11050_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2020.106685","volume":"117","author":"IdS J\u00fanior Tavares","year":"2020","unstructured":"J\u00fanior Tavares IdS, Torres CMME, Leite HG, Castro NLMd, Soares CPB, Castro RVO, Farias AA (2020) Machine learning: modeling increment in diameter of individual trees on atlantic forest fragments. Ecol Ind 117:106685. https:\/\/doi.org\/10.1016\/j.ecolind.2020.106685","journal-title":"Ecol Ind"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11050-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11050-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11050-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:27:55Z","timestamp":1762014475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11050-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,6]]},"references-count":70,"journal-issue":{"issue":"33","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["11050"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11050-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,4,6]]},"assertion":[{"value":"30 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}