Adaptive Pose Control for Robotic Manipulators Under Model Uncertainties with Application to Assembly Tasks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive Pose Control for Robotic Manipulators Under Model Uncertainties with Application to Assembly Tasks Can Liao, Hao Wen, Wei Zhang, Jingbo Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9264058/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This paper presents an adaptive pose control scheme for robotic manipulators performing assembly tasks under model uncertainties. The work focuses specifically on the approach phase of the assembly process, during which the manipulator moves from a relatively distant location to the vicinity of the assembly pose. In addition to model uncertainties inherent to the manipulator itself,this study addresses a particular type of uncertainty arising in assembly tasks:the inevitable discrepancy between the actual and intended gripping poses when grasping assembly modules. The proposed pose control scheme operates directly on Lie groups, thereby allowing position and orientation control to be handled in a geometrically consistent, non-singular and unified framework. It is constructed by combining a modified zeroing neural network (ZNN) joint controller with a Lie group-based kinematic estimator to counteract the impacts due to the model uncertainty of concern. To achieve faster convergence, a smooth saturation function is introduced to the modified ZNN controller instead of the conventional linear activation function. Theoretical analysis is conducted to analytically reveal the stability and convergence of the proposed scheme. Finally, comparative simulations and physical experiments on a 7-degree-of-freedom (7-DoF) manipulator validate the superiority and practical effectiveness of the proposed scheme. Pose control Model uncertainties Lie groups SE(3) Robotic assembly Neural dynamics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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