AI-Based Adaptive Control Systems for High-Precision Nano-Positioning Stages in Large-Scale XY Scanning

AI-Based Adaptive Control Systems for High-Precision Nano-Positioning Stages in Large-Scale XY Scanning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 May 2025 V1 Latest version Share on AI-Based Adaptive Control Systems for High-Precision Nano-Positioning Stages in Large-Scale XY Scanning Author : Emmanuel Idowu 0009-0009-4245-0599 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174733453.35306470/v1 195 views 153 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper presents an AI-based adaptive control system designed for high-precision nanopositioning stages in large-scale XY scanning applications. Achieving sub-nanometer resolution requires sophisticated control techniques to manage the inherent nonlinearities, external disturbances, and time-varying behavior of nano-positioning systems. To address these challenges, a deep reinforcement learning (DRL) agent is integrated with a baseline control framework (e.g., PID or sliding-mode control) to adaptively adjust control signals in real-time. Additionally, a recurrent neural network (RNN) is employed for predicting errors based on system behavior and compensating for actuator nonlinearities. Simulation and experimental results demonstrate that the proposed system significantly improves tracking accuracy, response time, and disturbance rejection compared to traditional control methods. The system also proves to be more energyefficient while maintaining high operational precision. Supplementary Material File (ai.pdf) Download 332.00 KB Information & Authors Information Version history V1 Version 1 15 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adaptive control artificial intelligence (ai) control systems disturbance rejection high-precision systems nano-positioning piezoelectric actuators recurrent neural networks (rnn) reinforcement learning xy scanning Authors Affiliations Emmanuel Idowu 0009-0009-4245-0599 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 195 views 153 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emmanuel Idowu. AI-Based Adaptive Control Systems for High-Precision Nano-Positioning Stages in Large-Scale XY Scanning. Authorea . 15 May 2025. DOI: https://doi.org/10.22541/au.174733453.35306470/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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