{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T14:03:33Z","timestamp":1771682613174,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This study proposes a novel adaptive control framework integrating nonlinear IoT dynamics with mechatronic systems to address the challenges of strong coupling, uncertainty, and real-time constraints. Our key innovation lies in a hierarchical optimization architecture combining model predictive control (MPC) with deep reinforcement learning (DRL), enabling dynamic adaptation through edge-cloud collaboration. Experimental results demonstrate efficiency improvements from 13.79% to 83.46% in subsystem performance, highlighting the algorithm's capability to balance robustness and adaptability. This work fills a critical gap in existing methods by unifying distributed sensing, online learning, and nonlinear optimization for IoT-enabled mechatronic systems. In contrast, the presence of high efficiency values, such as 93.76% and 80.75%, in certain parts of the system indicates that the system is able to achieve efficient operation under certain specific conditions. This study proposes a novel adaptive control framework integrating nonlinear IoT dynamics with mechatronic systems, leveraging Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), and Differential Evolution (DE) within a hierarchical optimization architecture. Experimental validation on a manipulator testbed demonstrates 83.46% efficiency improvement in subsystem coordination, &lt;100 ms response time under dynamic coupling, and 92.5% trajectory accuracy with \u00b15% standard deviation under 30% noise interference.<\/jats:p>","DOI":"10.31449\/inf.v50i7.9320","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:16:12Z","timestamp":1771679772000},"source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Adaptive Control of IoT-Integrated Mechatronic Systems Using Nonlinear Optimization and Edge-Cloud Collaboration"],"prefix":"10.31449","volume":"50","author":[{"given":"Zhibin","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rentao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaqin","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9320\/6508","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/9320\/6508","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:16:13Z","timestamp":1771679773000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/9320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i7.9320","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}