{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:24:22Z","timestamp":1773098662666,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper an active controller for ground vehicles stability is presented. The objective of this controller is to force the vehicle to track a desired reference, ensuring safe driving conditions in the case of adhesion loss during hazardous maneuvers. To this aim, a nonlinear discrete-time inverse optimal control based on a neural network identification is designed, using a recurrent high order neural network (RHONN) trained by an Extended Kalman Filter. The RHONN ensures stability of the identification error, while the controller ensures the stability of the tracking errors. Moreover, a discrete-time reduced order state observer is utilized to reconstruct the lateral vehicle dynamic not usually available. For the control problem, the references of the lateral velocity and yaw rate are given by a dynamic system mimicking an ideal vehicle having not-decreasing tire lateral characteristics. The proposed approach avoids the identification of the Pacejka\u2019s lateral parameters of the tires, so simplifying the input control determination. Moreover, an optimal control is proposed to optimize the actuator effort and power, usually bounded. Control gains are determined using optimal \u201cnature-inspired\" algorithms such as particle swarm optimization. Test maneuvers, performed through the full vehicle simulator CarSim<jats:sup>\u00ae<\/jats:sup>, have been used to test correctness, quality and performances of the observer, the neural identifier and the inverse optimal controller. Robustness of the reduced order discrete-time state observer is also discussed for different sample times. Finally, a fair comparison between optimal and non-optimal control schemes is presented, highlighting the numerical results obtained in simulation.<\/jats:p>","DOI":"10.1007\/s11063-023-11327-9","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T19:01:30Z","timestamp":1687287690000},"page":"10287-10313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Neural Network Inverse Optimal Control of Ground Vehicles"],"prefix":"10.1007","volume":"55","author":[{"given":"Riccardo","family":"Cespi","sequence":"first","affiliation":[]},{"given":"Stefano","family":"Di Gennaro","sequence":"additional","affiliation":[]},{"given":"Bernardino","family":"Castillo-Toledo","sequence":"additional","affiliation":[]},{"given":"Jorge Carlos","family":"Romero-Aragon","sequence":"additional","affiliation":[]},{"given":"Ricardo Ambrocio","family":"Ram\u00edrez-Mendoza","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"11327_CR1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1504\/IJVAS.2010.035792","volume":"8","author":"D Bianchi","year":"2010","unstructured":"Bianchi D, Borri A, Burgio G, Di Benedetto MD, Di Gennaro S (2010) Adaptive integrated vehicle control using active front steering and rear torque vectoring, special issue on: autonomous and semi-autonomous control for safe driving of ground vehicles. Int J Veh Auton Syst 8:85\u2013105","journal-title":"Int J Veh Auton Syst"},{"issue":"10","key":"11327_CR2","doi-asserted-by":"publisher","first-page":"6375","DOI":"10.1109\/TIE.2016.2578841","volume":"63","author":"A Navarrete Guzm\u00e1n","year":"2016","unstructured":"Navarrete Guzm\u00e1n A, Di Gennaro S, Rivera Dom\u00ednguez J, Acosta Lua C, Loukianov AG, Castillo-Toledo B (2016) Enhanced discrete-time modeling via variational integrators and digital controller design for ground vehicles. IEEE Trans Ind Electron 63(10):6375\u20136384","journal-title":"IEEE Trans Ind Electron"},{"key":"11327_CR3","doi-asserted-by":"publisher","first-page":"1722","DOI":"10.1016\/j.jfranklin.2016.12.012","volume":"354","author":"A Borri","year":"2017","unstructured":"Borri A, Bianchi D, Di Benedetto MD, Di Gennaro S (2017) Optimal workload actuator balancing and dynamic reference generation in active vehicle control. J Frankl Inst 354:1722\u20131740","journal-title":"J Frankl Inst"},{"key":"11327_CR4","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1007\/s12555-018-0484-0","volume":"18","author":"L Etienne","year":"2020","unstructured":"Etienne L, Acosta Lua C, Di Gennaro S, Barbot JP (2020) A super-twisting controller for active control of ground vehicles with lateral tire-road friction estimation and CarSim validation. Int J Control Autom Syst 18:1177\u20131189","journal-title":"Int J Control Autom Syst"},{"key":"11327_CR5","doi-asserted-by":"publisher","DOI":"10.1080\/00207179.2020.1791360","author":"C Abbas","year":"2020","unstructured":"Abbas C, Reine T, Moustapha D, Ali H, Ali C (2020) A comparison between a centralised multilayer LPV $$H_\\infty $$ and a decentralised multilayer sliding mode control architectures for vehicle\u2019s global chassis control. Int J Control. https:\/\/doi.org\/10.1080\/00207179.2020.1791360","journal-title":"Int J Control"},{"key":"11327_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2018.05.029","author":"J Zheng","year":"2018","unstructured":"Zheng J, Fu M, Lu R, Xie S (2018) Design, identification, and control of a linear dual-stage actuation positioning system. J Frankl Inst. https:\/\/doi.org\/10.1016\/j.jfranklin.2018.05.029","journal-title":"J Frankl Inst"},{"key":"11327_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2018.04.042","author":"G Perozzi","year":"2018","unstructured":"Perozzi G, Efimov D, Biannic JM, Planckaert L (2018) Trajectory tracking for a quadrotor under wind perturbations: sliding mode control with state-dependent gains. J Frankl Inst. https:\/\/doi.org\/10.1016\/j.jfranklin.2018.04.042","journal-title":"J Frankl Inst"},{"issue":"4","key":"11327_CR8","doi-asserted-by":"publisher","first-page":"2945","DOI":"10.1109\/TVT.2017.2782569","volume":"67","author":"M Ataei","year":"2018","unstructured":"Ataei M, Khajepour A, Jeon S (2018) A novel reconfigurable integrated vehicle stability control with omni actuation systems. IEEE Trans Veh Technol 67(4):2945\u20132957","journal-title":"IEEE Trans Veh Technol"},{"issue":"4","key":"11327_CR9","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1109\/TVT.2017.2778067","volume":"67","author":"W Liu","year":"2018","unstructured":"Liu W, Khajepour A, He H, Wang H, Huang Y (2018) Integrated torque vectoring control for a three-axle electric bus based on holistic cornering control method. IEEE Trans Veh Technol 67(4):2921\u20132933","journal-title":"IEEE Trans Veh Technol"},{"key":"11327_CR10","doi-asserted-by":"publisher","DOI":"10.1080\/00423114.2020.1869273","author":"M Vignati","year":"2021","unstructured":"Vignati M, Sabbioni E (2021) A cooperative control strategy for yaw rate and sideslip angle control combining torque vectoring with rear wheel steering. Veh Syst Dyn. https:\/\/doi.org\/10.1080\/00423114.2020.1869273","journal-title":"Veh Syst Dyn"},{"key":"11327_CR11","doi-asserted-by":"publisher","first-page":"4890","DOI":"10.1016\/j.jfranklin.2015.07.018","volume":"352","author":"C Acosta-Lua","year":"2015","unstructured":"Acosta-Lua C, Castillo-Toledo B, Cespi R, Di Gennaro S (2015) An integrated active nonlinear controller for wheeled vehicles. J Frankl Inst 352:4890\u20134910","journal-title":"J Frankl Inst"},{"key":"11327_CR12","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s12555-014-0193-2","volume":"14","author":"C Acosta-Lua","year":"2016","unstructured":"Acosta-Lua C, Castillo-Toledo B, Cespi R, Di Gennaro S (2016) Nonlinear observer-based active control of ground vehicles with non negligible roll dynamics. Int J Control Autom Syst 14:743\u2013752","journal-title":"Int J Control Autom Syst"},{"key":"11327_CR13","doi-asserted-by":"crossref","unstructured":"Bianchi D, Borri A, Burgio G, Di Benedetto MD, Di Gennaro S (2009) Adaptive integrated vehicle control using active front steering and rear torque vectoring. In: Joint 48th IEEE conference on decision and control and 28th Chinese control conference, pp 3557\u20133562","DOI":"10.1109\/CDC.2009.5400032"},{"key":"11327_CR14","doi-asserted-by":"publisher","unstructured":"Ghosh J, Tonoli A, Amati N (2017) Improvement of lap-time of a rear wheel drive electric racing vehicle by a novel motor torque control strategy. In: SAE technical paper. https:\/\/doi.org\/10.4271\/2017-01-0509","DOI":"10.4271\/2017-01-0509"},{"key":"11327_CR15","doi-asserted-by":"publisher","unstructured":"Ghosh J, and Tonoli A, Amati N. (2018) A deep learning based virtual sensor for vehicle sideslip angle estimation: experimental results. 2018. In: SAE technical paper. https:\/\/doi.org\/10.4271\/2018-01-1089","DOI":"10.4271\/2018-01-1089"},{"key":"11327_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0785-9","volume-title":"Adaptive control with recurrent high-order neural networks","author":"GA Rovithakis","year":"2000","unstructured":"Rovithakis GA, Chistodoulou MA (2000) Adaptive control with recurrent high-order neural networks. Springer, Berlin"},{"key":"11327_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-78289-6","volume-title":"Discrete-time high order neural control","author":"EN Sanchez","year":"2008","unstructured":"Sanchez EN, Alanis AY, Loukianov AG (2008) Discrete-time high order neural control. Springer, Berlin"},{"key":"11327_CR18","volume-title":"Discrete-time inverse optimal control for nonlinear systems","author":"EN Sanchez","year":"2013","unstructured":"Sanchez EN, Ornelas F (2013) Discrete-time inverse optimal control for nonlinear systems. CRC Press, Boca Raton"},{"key":"11327_CR19","volume-title":"Tire and vehicle dynamics","author":"HB Pacejka","year":"2005","unstructured":"Pacejka HB (2005) Tire and vehicle dynamics. Elsevier, Amsterdam"},{"key":"11327_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104275","volume":"104","author":"E Quintero-Manr\u00edquez","year":"2021","unstructured":"Quintero-Manr\u00edquez E, Sanchez EN, Antonio-Toledo ME, Mu\u00f1oz F (2021) Neural control of an induction motor with regenerative braking as electric vehicle architecture. Eng Appl Artif Intell 104:104275","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"11327_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/etep.2539","volume":"28","author":"L Djilali","year":"2018","unstructured":"Djilali L, Sanchez EN, Belkheiri M (2018) Real-time implementation of sliding-mode field-oriented control for a DFIG-based wind turbine. Int Trans Electr Energy Syst 28(5):e2539. https:\/\/doi.org\/10.1002\/etep.2539","journal-title":"Int Trans Electr Energy Syst"},{"key":"11327_CR22","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2018.5002","author":"L Djilali","year":"2019","unstructured":"Djilali L, Sanchez EN, Belkheiri M (2019) Real-time neural sliding mode field oriented control for a DFIG based wind turbine under balanced and unbalanced grid conditions. IET Renew Power Gener. https:\/\/doi.org\/10.1049\/iet-rpg.2018.5002","journal-title":"IET Renew Power Gener"},{"key":"11327_CR23","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ijrefrig.2017.04.01","volume":"79","author":"F Mu\u00f1oz","year":"2017","unstructured":"Mu\u00f1oz F, Sanchez EN, Xia Y, Deng S (2017) Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A\/C) system. Int J Refrig 79:196\u2013206. https:\/\/doi.org\/10.1016\/j.ijrefrig.2017.04.01","journal-title":"Int J Refrig"},{"key":"11327_CR24","doi-asserted-by":"publisher","unstructured":"Quintal G, Sanchez EN, Alanis AY, Arana-Daniel NG (2015) Real-time FPGA decentralized inverse optimal neural control for a Shrimp robot. In: 10th System of systems engineering conference (SoSE). https:\/\/doi.org\/10.1109\/sysose.2015.7151922","DOI":"10.1109\/sysose.2015.7151922"},{"issue":"4","key":"11327_CR25","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1109\/TCYB.2020.3004493","volume":"52","author":"C Mu","year":"2022","unstructured":"Mu C, Wang K, Qiu T (2022) Dynamic event-triggering neural learning control for partially unknown nonlinear systems. IEEE Trans Cybern 52(4):2200\u20132213. https:\/\/doi.org\/10.1109\/TCYB.2020.3004493","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"11327_CR26","doi-asserted-by":"publisher","first-page":"4437","DOI":"10.1109\/TNNLS.2021.3057438","volume":"33","author":"C Mu","year":"2022","unstructured":"Mu C, Wang K, Ni Z (2022) Adaptive learning and sampled-control for nonlinear game systems using dynamic event-triggering strategy. IEEE Trans Neural Netw Learn Syst 33(9):4437\u20134450. https:\/\/doi.org\/10.1109\/TNNLS.2021.3057438","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"11327_CR27","doi-asserted-by":"publisher","first-page":"3149","DOI":"10.1109\/TVT.2021.3064604","volume":"70","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang Y, Xu J, Chai T (2021) Observer-based discrete adaptive neural network control for automotive PEMFC air-feed subsystem. IEEE Trans Veh Technol 70(4):3149\u20133163. https:\/\/doi.org\/10.1109\/TVT.2021.3064604","journal-title":"IEEE Trans Veh Technol"},{"key":"11327_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104393","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang Y, Tie M (2021) Hybrid adaptive learning neural network control for steer-by-wire systems via sigmoid tracking differentiator and disturbance observer. Eng Appl Artif Intell. https:\/\/doi.org\/10.1016\/j.engappai.2021.104393","journal-title":"Eng Appl Artif Intell"},{"key":"11327_CR29","doi-asserted-by":"publisher","unstructured":"Wang Y, Wang Y (2021) Discrete-time adaptive neural network control for steer-by-wire systems with disturbance observer. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2021.115395","DOI":"10.1016\/j.eswa.2021.115395"},{"key":"11327_CR30","volume-title":"Redes neuronales: conceptos fundamentales y aplicaciones a control autom\u00e1tico. Autom\u00e1tica Rob\u00f3tica","author":"EN Sanchez","year":"2006","unstructured":"Sanchez EN, Alanis AY (2006) Redes neuronales: conceptos fundamentales y aplicaciones a control autom\u00e1tico. Autom\u00e1tica Rob\u00f3tica. Pearson Educaci\u00f3n, London"},{"key":"11327_CR31","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.1016\/j.neucom.2006.09.004","volume":"70","author":"J Rubio","year":"2006","unstructured":"Rubio J, Yu W (2006) Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm. Neurocomputing 70:2460\u20132466","journal-title":"Neurocomputing"},{"key":"11327_CR32","volume-title":"Kalman filtering and neural networks","author":"S Haykin","year":"2002","unstructured":"Haykin S (2002) Kalman filtering and neural networks. Wiley, New York"},{"key":"11327_CR33","first-page":"59","volume":"5","author":"Y Song","year":"1995","unstructured":"Song Y, Grizzle JW (1995) The extended Kalman filter as local asymptotic observer for discrete-time nonlinear systems. J Math Syst Estim Control 5:59\u201378","journal-title":"J Math Syst Estim Control"},{"key":"11327_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0967-9","volume-title":"Constructive nonlinear control","author":"R Sepulchre","year":"1997","unstructured":"Sepulchre R, Jankovic M, Kokotovic PV (1997) Constructive nonlinear control. Springer, Berlin"},{"key":"11327_CR35","volume-title":"Optimal control","author":"FL Lewis","year":"1995","unstructured":"Lewis FL, Syrmos VL (1995) Optimal control, 2nd edn. Wiley, New York","edition":"2"},{"key":"11327_CR36","volume-title":"Dynamic noncooperative game theory","author":"T Basar","year":"1995","unstructured":"Basar T, Olsder GJ (1995) Dynamic noncooperative game theory, 2nd edn. Academic Press, New York","edition":"2"},{"issue":"1","key":"11327_CR37","first-page":"63","volume":"14","author":"AY Alanis","year":"2010","unstructured":"Alanis AY, Sanchez EN, Loukianov AG (2010) Real-time discrete nonlinear identification via recurrent high order neural networks. Comput Sist 14(1):63\u201372","journal-title":"Comput Sist"},{"key":"11327_CR38","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1109\/TSMCB.2008.926614","volume":"38","author":"A Al-Tamimi","year":"2008","unstructured":"Al-Tamimi A, Lewis FL, Abu-Khalaf M (2008) Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof. IEEE Trans Syst Man Cybern-Part B 38:943\u2013949","journal-title":"IEEE Trans Syst Man Cybern-Part B"},{"key":"11327_CR39","volume-title":"Dynamic programming","author":"RE Bellman","year":"1957","unstructured":"Bellman RE (1957) Dynamic programming. Princeton University Press, Princeton"},{"key":"11327_CR40","unstructured":"Bellman RE, Dreyfus SE (1967) Programarea dinamic\u00e3 aplicat\u00e3, ser. Translated from the English. Editura Tehnic\u00e3, Bucharest"},{"key":"11327_CR41","unstructured":"Ornelas F, Loukianov AG, Sanchez EN, Bayro-Corrochano EJ (2008) Planar robot robust decentralized neural control. San Antonio, Texas, USA. In: Proceedings of the IEEE multiconference on systems and control (MSC 2008)"},{"key":"11327_CR42","unstructured":"Ricalde LJ (2005) Inverse optimal adaptive recurrent neural control with Constrained Inputs. Ph.D. Thesis, p 43"},{"issue":"6","key":"11327_CR43","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/j.jfranklin.2009.10.019","volume":"347","author":"F Ornelas","year":"2010","unstructured":"Ornelas F, Loukianov AG, S\u00e1nchez EN, Bayro-Corrochano EJ (2010) Decentralized neural identification and control for uncertain nonlinear systems: application to planar robot. J Frankl Inst 347(6):1015\u20131034","journal-title":"J Frankl Inst"},{"key":"11327_CR44","unstructured":"Guiggiani M (2007) Dinamica del veicolo. CittaStudi"},{"key":"11327_CR45","doi-asserted-by":"crossref","unstructured":"Heydinger GJ, Garrott WR, Chrstos JP, Guenther DA (1990) A methodology for validating vehicle dynamics simulations. SAE J Autom Eng. Paper 900128","DOI":"10.4271\/900128"},{"key":"11327_CR46","volume-title":"Theory of ground vehicles","author":"J Wong","year":"1993","unstructured":"Wong J (1993) Theory of ground vehicles. Wiley, New York"},{"key":"11327_CR47","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1016\/j.jfranklin.2017.01.020","volume":"354","author":"C Acosta L\u00faa","year":"2017","unstructured":"Acosta L\u00faa C, Di Gennaro S (2017) Nonlinear adaptive tracking for ground vehicles in the presence of lateral wind disturbances and parameter variations. J Frankl Inst 354:2742\u20132768","journal-title":"J Frankl Inst"},{"key":"11327_CR48","doi-asserted-by":"publisher","unstructured":"Li J, Zhang Y, Yi J (2012) A hybrid physical-dynamic tire\/road friction model. J Dyn Syst Meas Control. https:\/\/doi.org\/10.1115\/1.4006887","DOI":"10.1115\/1.4006887"},{"key":"11327_CR49","doi-asserted-by":"publisher","DOI":"10.1076\/vesd.39.3.189.14152","author":"C Canudas-de-Wit","year":"2003","unstructured":"Canudas-de-Wit C, Tsiotras P, Velenis E, Basset M, Gissinger G (2003) Dynamic friction models for road\/tire longitudinal interaction. Veh Syst Dyn. https:\/\/doi.org\/10.1076\/vesd.39.3.189.14152","journal-title":"Veh Syst Dyn"},{"key":"11327_CR50","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995\u2014international conference on neural Networks, Perth, WA, Australia, vol 4, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"11327_CR51","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-61520-666-7","volume-title":"Particle swarm optimization and intelligence: advances and applications","author":"KE Parsopoulos","year":"2010","unstructured":"Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications. Information Science Reference, Hersey"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11327-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11327-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11327-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T05:00:41Z","timestamp":1700629241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11327-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,20]]},"references-count":51,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11327"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11327-9","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,20]]},"assertion":[{"value":"5 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2023","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"There are no human subjects in this article and informed consent is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}