[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82542-en":3,"doc-seo-82542-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82542,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Learning-Based Control of a Single-DOF Aero System","A learning-based control framework integrates feedback linearization with reinforcement learning for adaptive control of nonlinear mechatronic systems subject to modeling uncertainties and external disturbances. Lyapunov stability analysis derives a controller that guarantees closed-loop stability under uncertainty. Reinforcement learning online estimates and compensates unmodeled dynamics using the REINFORCE-with-baseline algorithm, reducing policy-gradient variance for efficient, stable updates. The method is assessed on a single-DOF rotor-based AERO system, showing accurate trajectory tracking, rapid adaptation, and strong robustness.","arXiv :2607 .00640v1 [ ee ss . SY] 1 Jul 2026  \nEURODINAME III -An International Symposium on Dynamic Problems of Mechanics  \nJune 8-11, 2026-Giens Peninsula, FR  \nEURODINAME-2026-12929  \nLEARNING-BASED CONTROL OF A SINGLE-DOF AERO SYSTEM  \nGabriel da Silva Lima  \nWallace Moreira Bessa  \nSmart Systems Lab, Department of Mechanical Engineering, University of Turku, 20520, Finland.  \ngdasil@utu.fi, wmobes@utu.fi  \nAbstract. This paper presents a learning-based control framework that integrates feedback linearization with reinforce ment learning for the adaptive control of nonlinear mechatronic systems. The control law is derived using Lyapunov stability analysis, ensuring closed-loop stability in the presence of modeling uncertainties and external disturbances.  \nFeedback linearization serves as the main control framework, while a reinforcement learning component estimates and compensates for unmodeled dynamics and disturbances online. The learning module is based on the REINFORCE-withbaseline algorithm, which improves learning efficiency by reducing the variance of policy-gradient estimates and enabling stable policy updates during adaptation. The proposed controller is evaluated on a single-degree-of-freedom rotor-based AERO system. Results from simulations demonstrate accurate trajectory tracking, fast adaptation, and strong robust ness against parameter variations and external disturbances. Overall, the proposed approach combines the analytical guarantees of Lyapunov-based control with the adaptability of reinforcement learning, providing an effective solution for controlling nonlinear mechatronic systems.  \nKeywords: Learning-based control, feedback linearization, reinforcement learning, REINFORCE.  \n1. INTRODUCTION  \nNonlinear mechatronic systems are ubiquitous in modern engineering applications, including aerospace platforms, robotic manipulators, and electromechanical devices. The control of such systems is particularly challenging due to strong nonlinearities, parametric uncertainties, unmodeled dynamics, and external disturbances, which often degrade performance and compromise stability when conventional model-based controllers are applied (da Silva Lima et al., 2023; Misganaw et al., 2025) . Classical nonlinear control techniques, such as feedback linearization and sliding mode control, offer powerful tools to handle nonlinear dynamics and provide formal stability guarantees; however, their performance critically depends on the availability of accurate system models (Turab et al., 2025; Ismael et al., 2026) . In practice, modeling errors and unknown disturbances are unavoidable, motivating the development of adaptive, intelligent, and learning-based control strategies that can preserve stability while improving robustness and performance in uncertain environments.  \nWhile various adaptive and learning-based control strategies have been proposed to address modeling inaccuracies and disturbances, many approaches suffer from limitations related to convergence speed, sample efficiency, or lack of formal stability guarantees (Berkenkamp et al., 2017; Cao et al., 2025) . Reinforcement learning (RL), in particular, has emerged as a powerful framework for adaptive control due to its capacity to learn optimal policies through interaction with the environment (Sutton et al., 1998) . However, standard RL algorithms often require extensive exploration and generally do not provide formal guarantees of closed-loop stability, particularly when applied independently to safety-critical or highly nonlinear systems (Chow et al., 2018; Zhang et al., 2024) . These challenges have motivated the development of hybrid control strategies that combine the stability guarantees of model-based control techniques with the adaptability of learning-based components. In such frameworks, the model-based controller provides a stable foundation, while the learning module adapts to unmodeled dynamics and uncertainties. However, most RL-based controllers ","cbCaidUWcNOJ4WAR","https://ap.wps.com/l/cbCaidUWcNOJ4WAR","pdf",1656989,3,1,"English","en",105,"# Introduction\n# System Model","[{\"question\":\"What control architecture combines feedback linearization and reinforcement learning in this work?\",\"answer\":\"The proposed controller uses feedback linearization as the main framework and a reinforcement learning component to estimate and compensate for unmodeled dynamics and disturbances online.\"},{\"question\":\"How does the paper ensure stability when using learning for adaptive control?\",\"answer\":\"A Lyapunov stability analysis is used to derive the nominal control law, providing closed-loop stability even in the presence of modeling uncertainties and external disturbances.\"},{\"question\":\"Why use the REINFORCE-with-baseline algorithm in the learning module?\",\"answer\":\"The learning module is based on REINFORCE-with-baseline to improve learning efficiency by reducing variance in policy-gradient estimates, enabling stable policy updates during 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control architecture combines feedback linearization and reinforcement learning in this work?","Question",{"text":74,"@type":75},"The proposed controller uses feedback linearization as the main framework and a reinforcement learning component to estimate and compensate for unmodeled dynamics and disturbances online.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper ensure stability when using learning for adaptive control?",{"text":79,"@type":75},"A Lyapunov stability analysis is used to derive the nominal control law, providing closed-loop stability even in the presence of modeling uncertainties and external disturbances.",{"name":81,"@type":72,"acceptedAnswer":82},"Why use the REINFORCE-with-baseline algorithm in the learning module?",{"text":83,"@type":75},"The learning module is based on REINFORCE-with-baseline to improve learning efficiency by reducing variance in policy-gradient estimates, enabling stable policy updates during 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