{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:03:05Z","timestamp":1783573385737,"version":"3.55.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322151","type":"print"},{"value":"9783030322168","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32216-8_48","type":"book-chapter","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T12:14:33Z","timestamp":1571832873000},"page":"498-507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Adaptive PID Controlling Algorithm Using Asynchronous Advantage Actor-Critic Learning Method"],"prefix":"10.1007","author":[{"given":"Qifeng","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youxiang","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"48_CR1","doi-asserted-by":"crossref","unstructured":"Adel, T., Abdelkader, C.: A particle swarm optimization approach for optimum design of PID controller for nonlinear systems. In: International Conference on Electrical Engineering and Software Applications, pp. 1\u20134. IEEE (2013)","DOI":"10.1109\/ICEESA.2013.6578478"},{"issue":"5","key":"48_CR2","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1016\/j.asoc.2012.11.021","volume":"13","author":"A Savran","year":"2013","unstructured":"Savran, A.: A multivariable predictive fuzzy PID control system. Appl. Soft Comput. 13(5), 2658\u20132667 (2013)","journal-title":"Appl. Soft Comput."},{"key":"48_CR3","doi-asserted-by":"publisher","unstructured":"Jiang, D., Wang, W., Shi, L., Song, H.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. (2018). \n                  https:\/\/doi.org\/10.1109\/tnse.2018.2877597","DOI":"10.1109\/tnse.2018.2877597"},{"issue":"5","key":"48_CR4","first-page":"1","volume":"13","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS One 13(5), 1\u201323 (2018)","journal-title":"PLoS One"},{"issue":"5","key":"48_CR5","first-page":"1","volume":"2014","author":"X Zhang","year":"2014","unstructured":"Zhang, X., Bao, H., Du, J., et al.: Application of a new membership function in nonlinear fuzzy PID controllers with variable gains. Inf. Control 2014(5), 1\u20137 (2014)","journal-title":"Inf. Control"},{"issue":"1","key":"48_CR6","first-page":"167","volume":"32","author":"LI Cao-Cang","year":"2015","unstructured":"Cao-Cang, L.I., Zhang, C.F.: Adaptive neuron PID control based on minimum resource allocation network. Appl. Res. Comput. 32(1), 167\u2013169 (2015)","journal-title":"Appl. Res. Comput."},{"key":"48_CR7","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.procs.2015.06.023","volume":"54","author":"R Patel","year":"2015","unstructured":"Patel, R., Kumar, V.: Multilayer neuro PID controller based on back propagation algorithm. Procedia Comput. Sci. 54, 207\u2013214 (2015)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"48_CR8","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/S1006-1266(07)60009-1","volume":"17","author":"XS Wang","year":"2007","unstructured":"Wang, X.S., Cheng, Y.H., Wei, S.: A proposal of adaptive PID controller based on reinforcement learning. J. China Univ. Min. Technol. 17(1), 40\u201344 (2007)","journal-title":"J. China Univ. Min. Technol."},{"key":"48_CR9","unstructured":"Su, Y., Chen, L., Tang, C., et al.: Evolutionary multi-objective optimization of PID parameters for output voltage regulation in ECPT system based on NSGA-II. Trans. China Electrotech. Soc. 31(19), 106\u2013114 (2016)"},{"issue":"2","key":"48_CR10","first-page":"20","volume":"7","author":"A Akbarimajd","year":"2015","unstructured":"Akbarimajd, A.: Reinforcement learning adaptive PID controller for an under-actuated robot arm. Int. J. Integr. Eng. 7(2), 20\u201327 (2015)","journal-title":"Int. J. Integr. Eng."},{"issue":"8","key":"48_CR11","first-page":"1187","volume":"28","author":"XS Chen","year":"2011","unstructured":"Chen, X.S., Yang, Y.M.: A novel adaptive PID controller based on actor-critic learning. Control Theory Appl. 28(8), 1187\u20131192 (2011)","journal-title":"Control Theory Appl."},{"key":"48_CR12","unstructured":"Bahdanau, D., Brakel, P., Xu, K., et al.: An actor-critic algorithm for sequence prediction. arXiv preprint \n                  arXiv:1607.07086\n                  \n                 (2016)"},{"key":"48_CR13","unstructured":"Wang, Z., Bapst, V., Heess, N., et al.: Sample efficient actor-critic with experience replay. arXiv preprint \n                  arXiv:1611.01224\n                  \n                 (2016)"},{"key":"48_CR14","unstructured":"Mnih, V., Badia, A.P., Mirza, M., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928\u20131937 (2016)"},{"key":"48_CR15","doi-asserted-by":"publisher","first-page":"3305","DOI":"10.1109\/TITS.2017.2778939","volume":"19","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19, 3305\u20133319 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"01","key":"48_CR16","first-page":"1","volume":"41","author":"Q Liu","year":"2018","unstructured":"Liu, Q., et al.: A survey on deep reinforcement learning. Chin. J. Comput. 41(01), 1\u201327 (2018)","journal-title":"Chin. J. Comput."},{"issue":"9","key":"48_CR17","first-page":"1588","volume":"43","author":"R Qin","year":"2015","unstructured":"Qin, R., Zeng, S., Li, J.J., et al.: Parallel enterprises resource planning based on deep reinforcement learning. Zidonghua Xuebao\/Acta Autom. Sin. 43(9), 1588\u20131596 (2015)","journal-title":"Zidonghua Xuebao\/Acta Autom. Sin."},{"key":"48_CR18","unstructured":"Liao, F.F., Xiao, J.: Research on self-tuning of PID parameters based on BP neural networks. Acta Simulata Syst. Sin. 07, 1711\u20131713 (2005)"},{"key":"48_CR19","unstructured":"Guo-Yong, L.I., Chen, X.L.: Neural network self-learning PID controller based on real-coded genetic algorithm. Micromotors Servo Tech. 1, 43\u201345 (2008)"},{"key":"48_CR20","unstructured":"Sheng, X., Jiang, T., Wang, J., et al.: Speed-feed-forward PID controller design based on BP neural network. J. Comput. Appl. 35(S2), 134\u2013137 (2015)"},{"key":"48_CR21","unstructured":"Ma, L., Cai, Z.X.: Fuzzy adaptive controller based on reinforcement learning. Cent. South Univ. Technol. 29(2), 172\u2013176 (1998)"},{"issue":"3","key":"48_CR22","first-page":"579","volume":"52","author":"Z Liu","year":"2015","unstructured":"Liu, Z., Zeng, X., Liu, H., et al.: A heuristic two-layer reinforcement learning algorithm based on BP neural networks. J. Comput. Res. Dev. 52(3), 579\u2013587 (2015)","journal-title":"J. Comput. Res. Dev."},{"key":"48_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2013.08.037","volume":"261","author":"X Xu","year":"2014","unstructured":"Xu, X., Zuo, L., Huang, Z.: Reinforcement learning algorithms with function approximation: recent advances and applications. Inf. Sci. 261, 1\u201331 (2014)","journal-title":"Inf. Sci."},{"key":"48_CR24","first-page":"1711","volume":"S1","author":"SY Yang","year":"2007","unstructured":"Yang, S.Y., Xu, L.P., Wang, P.J.: Study on PID control of a single inverted pendulum system. Control Eng. China S1, 1711\u20131713 (2007)","journal-title":"Control Eng. China"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Simulation Tools and Techniques"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32216-8_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,9]],"date-time":"2020-02-09T00:22:52Z","timestamp":1581207772000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-32216-8_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322151","9783030322168"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32216-8_48","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMUtools","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Simulation Tools and Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simutools2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/simutools.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"156","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":"97","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":"62% - 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":"3","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}