{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:04:37Z","timestamp":1750309477224,"version":"3.41.0"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSF of China","doi-asserted-by":"crossref","award":["62103268, 92167205, 62202287, 61933009, 62025305"],"award-info":[{"award-number":["62103268, 92167205, 62202287, 61933009, 62025305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Cyber-Phys. Syst."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n            Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the \u201cshort-sightedness\u201d of single-step performance prediction, we extend ADPS to ADPS-m (\n            <jats:italic>m<\/jats:italic>\n            ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H (\n            <jats:italic>H<\/jats:italic>\n            eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m (\n            <jats:italic>m<\/jats:italic>\n            ulti-step prediction for\n            <jats:italic>H<\/jats:italic>\n            eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB\/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(-\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.\n          <\/jats:p>","DOI":"10.1145\/3685933","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T16:21:04Z","timestamp":1722615664000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1468-7451","authenticated-orcid":false,"given":"Ruijie","family":"Fu","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4923-4110","authenticated-orcid":false,"given":"Lancong","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0083-5281","authenticated-orcid":false,"given":"An","family":"Zou","sequence":"additional","affiliation":[{"name":"University of Michigan \u2013 Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6533-8713","authenticated-orcid":false,"given":"Cailian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Automation and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1858-8538","authenticated-orcid":false,"given":"Xinping","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Automation and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8595-1619","authenticated-orcid":false,"given":"Yehan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","unstructured":"IEEE Standard for Low-Rate Wireless Networks. 2020. 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