{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:57:48Z","timestamp":1742943468694,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819964949"},{"type":"electronic","value":"9789819964956"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-6495-6_38","type":"book-chapter","created":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T18:01:56Z","timestamp":1697392916000},"page":"445-455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Review of Nonlinear Systems Based on Optimal Control Theory"],"prefix":"10.1007","author":[{"given":"Xiaodan","family":"Lu","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2020.109068","volume":"119","author":"L Hazeleger","year":"2020","unstructured":"Hazeleger, L., Haring, M., Wouw, N.: Extremum-seeking control for optimization of time-varying steady-state responses of nonlinear systems. Automatica 119, 109068 (2020)","journal-title":"Automatica"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Zhu, F., Zhong, P., Sun, Y., et al.: A coordinated optimization framework for long-term complementary operation of a large-scale hydro-photovoltaic hybrid system: Nonlinear modeling, multi-objective optimization and robust decision-making. Energy Conversion and Management 226, 113543 (2020)","DOI":"10.1016\/j.enconman.2020.113543"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Mirzaei, A., Ramezani, A.: Cooperative optimization-based distributed model predictive control for constrained nonlinear large-scale systems with stability and feasibility guarantees. ISA Transactions (2021)","DOI":"10.1016\/j.isatra.2021.01.022"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Fu J., Tian, F.: Dynamic optimization of nonlinear systems with guaranteed feasibility of inequality-path-constraints. Automatica 127, 109516 (2021)","DOI":"10.1016\/j.automatica.2021.109516"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Wen, G., Chen, C., Li, W.N.: Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems. Information Sciences 517(1) (2019)","DOI":"10.1016\/j.ins.2019.12.039"},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Sahoo, A., Narayanan,V.: Optimization of sampling intervals for tracking control of nonlinear systems: a game theoretic approach. Neural Networks 114, 78\u201390 (2019)","DOI":"10.1016\/j.neunet.2019.02.008"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Liu, X, Zhao, B., Liu, D.: Fault tolerant tracking control for nonlinear systems with actuator failures through particle swarm optimization- based adaptive dynamic programming. Applied Soft Computing 97, 106766 (2020)","DOI":"10.1016\/j.asoc.2020.106766"},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, W., Xie, X.J., Liang, J.: Neural-Network-based Optimization and Analysis for Nonlinear Stochastic Systems. Neurocomputing 452, 779\u2013780 (2020)","DOI":"10.1016\/j.neucom.2020.05.079"},{"key":"38_CR9","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2020.09.020","volume":"134","author":"ZA Bo","year":"2021","unstructured":"Bo, Z.A., Fl, B., Hl, B., et al.: Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems. Neural Netw. 134, 54\u201363 (2021)","journal-title":"Neural Netw."},{"key":"38_CR10","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.matcom.2020.10.004","volume":"181","author":"A Ij","year":"2021","unstructured":"Ij, A., Mazrb, C., Mj, C., et al.: Design of evolutionary optimized finite difference based numerical computing for dust density model of nonlinear Van-der Pol Mathieu\u2019s oscillatory systems. Math. Comput. Simul. 181, 444\u2013470 (2021)","journal-title":"Math. Comput. Simul."},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Chakrabarty, A., Benosman, M.: Safe learning-based observers for unknown nonlinear systems using bayesian optimization. Elec. Eng. Sys. Sci. Sys. Cont. (2020)","DOI":"10.1016\/j.automatica.2021.109860"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"He, S., Fang, H., Zhang, M., et al.: Online policy iterative-based H\u221e optimization algorithm for a class of nonlinear systems. Information Sciences 495 (2019)","DOI":"10.1016\/j.ins.2019.04.027"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhong, Z., Lin, C.M., et al.: Tracking control for nonlinear multivariable systems using wavelet-type TSK fuzzy brain emotional learning with particle swarm optimization. Journal of the Franklin Institute 358(1) (2020)","DOI":"10.1016\/j.jfranklin.2020.10.047"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Liu, H, Tong, Z.: Robust state estimation for uncertain linear systems with random parametric uncertainties. Sci. China Info. Sci. 60(1), 1\u201313 (2017)","DOI":"10.1007\/s11432-015-0327-x"},{"key":"38_CR15","first-page":"1","volume":"99","author":"S Li","year":"2020","unstructured":"Li, S., Xia, W., Zhang, F.: Synchronization of continuous-time linear systems with time-varying output couplings. IEEE Trans. Industr. Inf. 99, 1 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Ji, J.Y., Man, L.W.: An improved dynamic multi-objective optimization approach for nonlinear equation systems. Information Sciences 576, 204\u2013227 (2021)","DOI":"10.1016\/j.ins.2021.06.070"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Gao, W., Luo, Y., Xu, J., et al.: Evolutionary algorithm with multi objective optimization technique for solving nonlinear equation systems. Information Sciences 541(8) (2020)","DOI":"10.1016\/j.ins.2020.06.042"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Hendriks, J.N., Holdsworth, J., Wills, A.G., et al.: Data to Controller for Nonlinear Systems: an Approximate Solution (2021)","DOI":"10.1109\/LCSYS.2021.3090349"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6495-6_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T18:05:07Z","timestamp":1697393107000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6495-6_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819964949","9789819964956"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6495-6_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icira2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"630","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"431","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}