Intelligent Control Algorithms for Precision Nanopositioning Systems

Intelligent Control Algorithms for Precision Nanopositioning Systems | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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 Intelligent Control Algorithms for Precision Nanopositioning Systems Author : Emmanuel Idowu 0009-0009-4245-0599 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174733468.88100146/v1 175 views 152 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Precision nanopositioning systems are essential for nanoscale applications such as atomic force microscopy, biomedical imaging, and nanofabrication. These systems often employ piezoelectric actuators, which are highly nonlinear due to phenomena such as hysteresis and creep. Conventional control techniques, including PID and feedforward methods, are limited in their ability to address these challenges under dynamic operating conditions. This study explores the integration of intelligent control algorithms-specifically fuzzy logic, artificial neural networks (ANNs), and reinforcement learning (RL)-to improve positioning accuracy, adaptability, and system robustness. A comprehensive model of the nanopositioning stage is developed, incorporating actuator dynamics and system uncertainties. The proposed algorithms are implemented in both simulation and hardware-in-the-loop setups. Results show that intelligent controllers outperform classical methods in terms of tracking precision, disturbance rejection, and adaptability to nonlinear behaviors. The research highlights the potential of hybrid intelligent controllers for advanced precision applications and offers a framework for real-time implementation in industrial systems. Supplementary Material File (intelligent control algorithms for precision nanop.pdf) Download 236.68 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 fuzzy logic hysteresis intelligent control nanopositioning neural networks nonlinear systems piezoelectric actuators precision engineering reinforcement learning Authors Affiliations Emmanuel Idowu 0009-0009-4245-0599 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 175 views 152 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emmanuel Idowu. Intelligent Control Algorithms for Precision Nanopositioning Systems. Authorea . 15 May 2025. DOI: https://doi.org/10.22541/au.174733468.88100146/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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