mlsanityData to 14 Oct 2026

Learning-based control of a single-DOF Aero system

arXiv 2607.00640 ↗First posted 1 Jul 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper presents a learning-based control framework that integrates feedback linearization with reinforcement 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. Feedback 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-with-baseline 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 robustness 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 11 references that are in the map. First posted on arXiv 1 Jul 2026.

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3 of 11 references.

References · 3

  1. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  2. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  3. 2021Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehiclescited by 4

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