Optimization control of the double‐capacity water tank‐level system using the deep deterministic policy gradient algorithm
Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly affect the system's stability and overall performance. The application and promotion of intelligent control algorithms with self‐learning, self‐op...
Uložené v:
| Vydané v: | Engineering reports (Hoboken, N.J.) Ročník 5; číslo 11 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Hoboken
John Wiley & Sons, Inc
01.11.2023
Wiley |
| Predmet: | |
| ISSN: | 2577-8196, 2577-8196 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly affect the system's stability and overall performance. The application and promotion of intelligent control algorithms with self‐learning, self‐optimization, and self‐adaption characteristics have thus become a challenging yet meaningful research topic. In this article, we propose a novel approach that incorporates the deep deterministic policy gradient (DDPG) algorithm into the control of double‐capacity water tanklevel system. Specifically, we introduce a fully connected layer on the observer side of the critic network to enhance its expression capability and processing efficiency, allowing for the extraction of important features for water‐level control. Additionally, we optimize the node parameters of the neural network and use the RELU activation function to ensure the network's ability to continuously observe and learn from the external water tank environment while avoiding the issue of vanishing gradients. We enhance the system's feedback regulation ability by adding the PID controller output to the observer input based on the liquid level deviation and height. This integration with the DDPG control method effectively leverages the benefits of both, resulting in improved robustness and adaptability of the system. Experimental results show that our proposed model outperforms traditional control methods in terms of convergence, tracking, anti‐disturbance and robustness performances, highlighting its effectiveness in improving the stability and precision of double‐capacity water tank systems. |
|---|---|
| AbstractList | Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly affect the system's stability and overall performance. The application and promotion of intelligent control algorithms with self-learning, self-optimization, and self-adaption characteristics have thus become a challenging yet meaningful research topic. In this article, we propose a novel approach that incorporates the deep deterministic policy gradient (DDPG) algorithm into the control of double-capacity water tanklevel system. Specifically, we introduce a fully connected layer on the observer side of the critic network to enhance its expression capability and processing efficiency, allowing for the extraction of important features for water-level control. Additionally, we optimize the node parameters of the neural network and use the RELU activation function to ensure the network's ability to continuously observe and learn from the external water tank environment while avoiding the issue of vanishing gradients. We enhance the system's feedback regulation ability by adding the PID controller output to the observer input based on the liquid level deviation and height. This integration with the DDPG control method effectively leverages the benefits of both, resulting in improved robustness and adaptability of the system. Experimental results show that our proposed model outperforms traditional control methods in terms of convergence, tracking, anti-disturbance and robustness performances, highlighting its effectiveness in improving the stability and precision of double-capacity water tank systems. Abstract Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly affect the system's stability and overall performance. The application and promotion of intelligent control algorithms with self‐learning, self‐optimization, and self‐adaption characteristics have thus become a challenging yet meaningful research topic. In this article, we propose a novel approach that incorporates the deep deterministic policy gradient (DDPG) algorithm into the control of double‐capacity water tanklevel system. Specifically, we introduce a fully connected layer on the observer side of the critic network to enhance its expression capability and processing efficiency, allowing for the extraction of important features for water‐level control. Additionally, we optimize the node parameters of the neural network and use the RELU activation function to ensure the network's ability to continuously observe and learn from the external water tank environment while avoiding the issue of vanishing gradients. We enhance the system's feedback regulation ability by adding the PID controller output to the observer input based on the liquid level deviation and height. This integration with the DDPG control method effectively leverages the benefits of both, resulting in improved robustness and adaptability of the system. Experimental results show that our proposed model outperforms traditional control methods in terms of convergence, tracking, anti‐disturbance and robustness performances, highlighting its effectiveness in improving the stability and precision of double‐capacity water tank systems. |
| Author | Ye, Likun Jiang, Pei |
| Author_xml | – sequence: 1 givenname: Likun orcidid: 0000-0002-6689-1286 surname: Ye fullname: Ye, Likun organization: Shien‐Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou City Guangdong Province China – sequence: 2 givenname: Pei surname: Jiang fullname: Jiang, Pei organization: School of Instrument Science and Optoelectronic Engineering Beihang University Beijing City China |
| BookMark | eNptUctqHDEQFMGGOLYv-QJBboF19NgZzRyDycNg8MU5ix5Na6yNRppI2oT1IeQT8o35kmh3YwghF7VoqqqLqhfkJMSAhLzk7IozJt5gmMQVF23bPSNnolFq1fG-Pfnr_5xc5rxhFcwVZ5Kdke93S3Gze4TiYqAmhpKip9HS8oB0jNvB468fPw0sYFzZ0W9QMNEC4XPdevyKnuZdLjjTbXZhOrIQl_pU4OyCy8UZukTvzI5OCUaHoVDwU0yuPMwX5NSCz3j5Z56TT-_f3V9_XN3efbi5fnu7MrLlZcW7hhnBrGywY_0wcNM00Cvb23UdYhQjjHawfLTKynUrrOJd11rkgwVQVshzcnPUHSNs9JLcDGmnIzh9WMQ0aUjVqUctOTR8kEoOrVlzi71FBdjzkXWdFcNe69VRa0nxyxZz0Zu4TaHa16JeVTVn2VcUO6JMijkntLoGeEi5JHBec6b3pel9afpQWqW8_ofyZPQ_4N96Ep8s |
| CitedBy_id | crossref_primary_10_2166_hydro_2024_020 crossref_primary_10_1007_s43621_025_01832_3 crossref_primary_10_2478_ijssis_2025_0029 crossref_primary_10_1080_23307706_2024_2439520 crossref_primary_10_1007_s11081_025_09990_z |
| Cites_doi | 10.1109/ACCESS.2022.3176608 10.1109/ACCESS.2021.3096208 10.1109/JIOT.2019.2921159 10.1109/ACCESS.2020.3025194 10.1016/j.dche.2022.100023 10.1109/TII.2019.2894282 10.1016/j.compchemeng.2020.107133 10.1109/TCAD.2018.2878183 10.1016/j.trc.2018.12.018 10.1016/j.asoc.2021.107295 10.3390/jsan10040060 10.1109/ACCESS.2020.2968595 10.1109/ACCESS.2022.3180504 10.1007/s00521-022-07033-7 10.1063/5.0032377 10.1109/ACCESS.2022.3174625 10.1016/j.egyr.2022.02.231 10.1016/j.compchemeng.2019.106610 10.1109/JIOT.2021.3091508 |
| ContentType | Journal Article |
| Copyright | 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS DOA |
| DOI | 10.1002/eng2.12668 |
| DatabaseName | CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Engineering Collection DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2577-8196 |
| EndPage | n/a |
| ExternalDocumentID | oai_doaj_org_article_31a51b373b6c41fe9fe7ae91d088f2b2 10_1002_eng2_12668 |
| GroupedDBID | 0R~ 1OC 24P AAMMB AAYXX ABJCF ACCMX ACXQS ADKYN ADMLS ADZMN AEFGJ AFFHD AFKRA AGXDD AIDQK AIDYY ALMA_UNASSIGNED_HOLDINGS ALUQN ARCSS AVUZU BENPR BGLVJ CCPQU CITATION EBS EJD GROUPED_DOAJ HCIFZ IAO IGS ITC M7S M~E OK1 PHGZM PHGZT PIMPY PQGLB PTHSS WIN 8FE 8FG ABUWG AZQEC DWQXO L6V PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c361t-1850c20f35e809bb1c55a97f9f4a972d2dadfbf1df7f3462f71886fe1bfaa7f23 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000979823000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2577-8196 |
| IngestDate | Fri Oct 03 12:28:23 EDT 2025 Wed Aug 13 06:29:08 EDT 2025 Sat Nov 29 03:20:55 EST 2025 Tue Nov 18 20:48:43 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c361t-1850c20f35e809bb1c55a97f9f4a972d2dadfbf1df7f3462f71886fe1bfaa7f23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-6689-1286 |
| OpenAccessLink | https://www.proquest.com/docview/2886719639?pq-origsite=%requestingapplication% |
| PQID | 2886719639 |
| PQPubID | 5066167 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_31a51b373b6c41fe9fe7ae91d088f2b2 proquest_journals_2886719639 crossref_citationtrail_10_1002_eng2_12668 crossref_primary_10_1002_eng2_12668 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-11-00 20231101 2023-11-01 |
| PublicationDateYYYYMMDD | 2023-11-01 |
| PublicationDate_xml | – month: 11 year: 2023 text: 2023-11-00 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken |
| PublicationPlace_xml | – name: Hoboken |
| PublicationTitle | Engineering reports (Hoboken, N.J.) |
| PublicationYear | 2023 |
| Publisher | John Wiley & Sons, Inc Wiley |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley |
| References | e_1_2_9_10_1 Lynch MC (e_1_2_9_17_1) 2016 e_1_2_9_13_1 e_1_2_9_12_1 Efheij H (e_1_2_9_9_1) 2019 Dey N (e_1_2_9_8_1) 2013; 5 e_1_2_9_15_1 Li L (e_1_2_9_5_1) 2021; 405 e_1_2_9_14_1 Chai TY (e_1_2_9_6_1) 2020; 46 e_1_2_9_16_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_7_1 Zhou ZH (e_1_2_9_2_1) 2015 e_1_2_9_4_1 e_1_2_9_3_1 Venkataraman K (e_1_2_9_11_1) 2019 Xu TY (e_1_2_9_23_1) 2021 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 |
| References_xml | – ident: e_1_2_9_16_1 doi: 10.1109/ACCESS.2022.3176608 – ident: e_1_2_9_22_1 doi: 10.1109/ACCESS.2021.3096208 – volume: 46 start-page: 2005 issue: 10 year: 2020 ident: e_1_2_9_6_1 article-title: Development direction of industrial artificial intelligence publication-title: Journal of Automation – volume: 405 start-page: 1 issue: 3 year: 2021 ident: e_1_2_9_5_1 article-title: Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: a review publication-title: Chem Eng J – ident: e_1_2_9_19_1 doi: 10.1109/JIOT.2019.2921159 – ident: e_1_2_9_28_1 doi: 10.1109/ACCESS.2020.3025194 – ident: e_1_2_9_14_1 doi: 10.1016/j.dche.2022.100023 – start-page: 64 volume-title: 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) year: 2019 ident: e_1_2_9_9_1 – ident: e_1_2_9_24_1 doi: 10.1109/TII.2019.2894282 – ident: e_1_2_9_15_1 doi: 10.1016/j.compchemeng.2020.107133 – volume: 5 start-page: 2277 issue: 3 year: 2013 ident: e_1_2_9_8_1 article-title: Design and implementation of a water level controller using fuzzy logic publication-title: Int J Eng Technol – ident: e_1_2_9_20_1 doi: 10.1109/TCAD.2018.2878183 – volume-title: Machine Learning year: 2015 ident: e_1_2_9_2_1 – ident: e_1_2_9_12_1 doi: 10.1016/j.trc.2018.12.018 – ident: e_1_2_9_26_1 doi: 10.1016/j.asoc.2021.107295 – ident: e_1_2_9_4_1 doi: 10.3390/jsan10040060 – ident: e_1_2_9_10_1 doi: 10.1109/ACCESS.2020.2968595 – ident: e_1_2_9_21_1 doi: 10.1109/ACCESS.2022.3180504 – start-page: 4210 volume-title: Recovering Robustness in Model‐free Reinforcement Learning year: 2019 ident: e_1_2_9_11_1 – ident: e_1_2_9_25_1 doi: 10.1007/s00521-022-07033-7 – start-page: 11480 volume-title: Proceedings of the 38th International Conference on Machine Learning year: 2021 ident: e_1_2_9_23_1 – ident: e_1_2_9_7_1 doi: 10.1063/5.0032377 – start-page: 1 year: 2016 ident: e_1_2_9_17_1 publication-title: Int Conf Learn Rep – ident: e_1_2_9_3_1 doi: 10.1109/ACCESS.2022.3174625 – ident: e_1_2_9_18_1 doi: 10.1016/j.egyr.2022.02.231 – ident: e_1_2_9_27_1 doi: 10.1016/j.compchemeng.2019.106610 – ident: e_1_2_9_13_1 doi: 10.1109/JIOT.2021.3091508 |
| SSID | ssj0002171030 |
| Score | 2.283768 |
| Snippet | Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly affect the... Abstract Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| SubjectTerms | Algorithms Artificial intelligence Batch processes Chemical oxygen demand Comparative analysis Compensation Control algorithms Control methods Control systems Controllers DDPG adaptive compensation control DDPG pure control Energy consumption Industrial production Liquid levels Machine learning Methods Neural networks Optimization Perturbation process control system Process controls Proportional integral derivative reinforcement learning Robotics Robustness Water levels Water tanks |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Na9wwEBUl5NAcStI0dPNRBM2lBycrybbsYxu65FDSHlrIzehjZhu62V28TnIL-Qn5jf0lHUneZUMKvfRigxhjMzOa9wyjN4wdhynaWILLjC9tllvrMkplkYEWhdc6gFxU1_-iLy6qy8v629qor9ATluSBk-NOlTCFsEorW7pcINQI2kAtPG0PlDZWX2I9az9ToQYT0Q7zs1Z6pPIUpmN5IgiOqicIFIX6n9XhCC6jbfaqZ4X8Y_qaHfYCpq_Z1ppW4C67_0qb-7o_Ncn7DnM-Q04MjvvZjZ3A74dHR9jniFjzO-KQLSfm94tWJ6EziCfRZh463cfpKYA5XVI_TBRs5vMoE8zHbewE67iZjGftVffz-g37Mfr8_ew864cnZE6VossIh4dODlEVUA1ra4UrClNrrDGnm_TSG48WhUeNKi8lEkhVJYKwaIxGqfbYxnQ2hbeMeweVQUmBUza3WtcOdAVFiV4pXzg1YB-WDm1cryweBlxMmqSJLJvg_CY6f8Der2znSU_jr1afQlxWFkEDOy5QZjR9ZjT_yowBO1xGtek35qKRVRD0o6pT7_-Pdxywl2H-fDqceMg2uvYGjtimu-2uFu27mJN_AO4K7bU priority: 102 providerName: Directory of Open Access Journals |
| Title | Optimization control of the double‐capacity water tank‐level system using the deep deterministic policy gradient algorithm |
| URI | https://www.proquest.com/docview/2886719639 https://doaj.org/article/31a51b373b6c41fe9fe7ae91d088f2b2 |
| Volume | 5 |
| WOSCitedRecordID | wos000979823000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: DOA dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: M~E dateStart: 20190101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: M7S dateStart: 20191201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: BENPR dateStart: 20191201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: PIMPY dateStart: 20191201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: WIN dateStart: 20190101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2577-8196 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171030 issn: 2577-8196 databaseCode: 24P dateStart: 20190101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZgywEO5VmxpawswYWD6dpO4uSEKGpFJVhWPEQ5RX5NQGw3SzYFiV_P2PEuIBAXLoniOJKlGc-MJp-_j5CHQUUbCm-ZdoVhmTGWoStz5hXPnVIhyUV2_RdqNivPzqp5aritE6xyExNjoHatDT3yQ1EGJjZ0l-rJ6gsLqlHh72qS0LhMdgJTWTYiO0fHs_nrbZcFC-6go7XlJRWHftmIxxzTUvlbJoqE_X_E45hkTq7_7_JukN1UXtKngz_cJJf88ha59gvp4G3y_RVGifN0_JImqDptgWIpSF17YRaeWUyhFutz-g1L0Y5iAfmZLQK8iA7MzzTA5ZvhC-9XeBlANZH1ma4i1zBtuggn66leNLjU_uP5HfLu5Pjts-csKTAwKwveM0zmUyumIHNfTitjuM1zXSmoIMObcMJpBwa4AwUyKwRgpisL8NyA1gqE3COjZbv0dwl11pcaBFpfmswoVVmvSp8X4KR0uZVj8mhjjdomevKgkrGoB2JlUQfL1dFyY_JgO3c1kHL8ddZRMOp2RiDSjgNt19RpX9aS65wbqaQpbMbBV-CV9hV3GH1BGDEmBxt712l3r-ufxt7_9-t75GqQpx_OLh6QUd9d-Pvkiv3af1p3k-Ssk9gHmATU6Rscm5--nH_Ap_ensx_AOP38 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5VWyTgwBuxUMAScOBguraTODkgxKvqqttlD0UqpxC_0ortZsmmVPCj-I2MnWQBgbj1wCWRkskh9pdvxs7MNwCPfRdtl1hNC5MoGimlKUKZUStZbKT0Ti6o60_kdJoeHmazDfje18L4tMqeEwNRm0r7PfJtnnolNoRL9mL5mfquUf7vat9Co4XFnv16hku21fPxG5zfJ5zvvD14vUu7rgJUi4Q1FB3USPORE7FNR5lSTMdxkUmXuQhP3HBTGKccM046ESXcIXunibNMuaKQzgsdIOVvRgj2dACbs_H-7MN6VwcDfN-3a62DyrftouTPGLrB9DfPFxoE_MH_wantXP3fhuMaXOnCZ_Kyxft12LCLG3D5F1HFm_DtHbLgSVdeSrpUfFI5gqEuMdWpmluqMUTQuP4gZxhq1wQD5E907tOnSKtsTXw5QNk-Ye0SD23SUFC1JsugpUzKOqTLNaSYlzg0zdHJLXh_Li9_GwaLamHvADHapoXjiG6hIiVlpq1MbZw4I4SJtRjC0372c93Jr_suIPO8FY7muUdKHpAyhEdr22UrOvJXq1ceRGsLLxQeLlR1mXe8kwtWxEwJKVSiI-Zs5qwsbMYMehfHFR_CVo-vvGOvVf4TXHf_ffshXNw92J_kk_F07x5c4hgAtnWaWzBo6lN7Hy7oL83xqn7QfSgEPp43GH8AAgxYkA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwELWqghAc-EYsFLAEHDiYXdtJnBwQAsqKqtVSCZB6M_HHhKrbzZJNqeCn8esYO8kCAnHrgUsiJZNDnOeZZ2fmDSGPQhdtyLxlpcsMS4yxDKHMmVc8dUqFIBfV9ffUbJYfHBT7G-T7UAsT0ioHnxgdtatt2CMfizwosSFcijH0aRH729Pny88sdJAKf1qHdhodRHb911Ncvq2e7Wzjt34sxPT1-1dvWN9hgFmZ8ZZhsJpYMQGZ-nxSGMNtmpaFggISPAknXOnAAHegQCaZAPTkeQaeGyhLBUH0AN3_OZUgT4hpg-_W-ztI9UMHr7Uiqhj7RSWecgyI-W8xMLYK-CMSxPA2vfI_D8xVcrkn1fRFNwuukQ2_uE4u_SK1eIN8e4u-8bgvOqV9gj6tgSIBpq4-MXPPLBIHi6sSeooEvKFIm4_YPCRV0U7vmoYigap7wvslHrpUoqh1TZdRYZlWTUyia2k5r3Bo2k_HN8mHM3n5W2RzUS_8bUKd9XkJAjEvTWKUKqxXuU8zcFK61MoReTIgQdtelD30BpnrTk5a6IAaHVEzIg_XtstOiuSvVi8DoNYWQT48XqibSvfeSEteptxIJU1mEw6-AK9KX3CHMQeEESOyNWBN9z5tpX8C7c6_bz8gFxCBem9ntnuXXBTICrvizS2y2TYn_h45b7-0h6vmfpwxlHw8ayT-AM2DX9c |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimization+control+of+the+double-capacity+water+tank-level+system+using+the+deep+deterministic+policy+gradient+algorithm&rft.jtitle=Engineering+reports+%28Hoboken%2C+N.J.%29&rft.au=Ye%2C+Likun&rft.au=Jiang%2C+Pei&rft.date=2023-11-01&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.eissn=2577-8196&rft.volume=5&rft.issue=11&rft_id=info:doi/10.1002%2Feng2.12668&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2577-8196&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2577-8196&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2577-8196&client=summon |