mlsanityData to 14 Oct 2026

Safe Reinforcement Learning Beyond Baseline Control: A Hierarchical Framework for Space Triangle Tethered Formation System

arXiv 2601.04957 ↗First posted 8 Jan 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Triangular tethered formation system (TTFS) provide a promising platform for deep space exploration and distributed sensing due to its intrinsic spatial-orientation stability and capability of adjusting distances among node satellites through deployment and retrieval of tethers. However, due to the coupled tether-satellite dynamics and disturbance sensitivity of TTFS, traditional control methods struggle to achieve a balanced trade-off among configuration accuracy requirements, tension constraints, and energy efficiency consumption throughout the deployment process.In this paper, a novel model-reference reinforcement learning control framework is proposed for TTFS. By integrating baseline model-based control with a Soft Actor-Critic (SAC) compensator, the proposed method simultaneously achieves high-precision tracking, fuel efficiency, and compliance with tension limits. A hierarchical training scheme is developed to address the convergence difficulties arising from strongly coupled states in centralized training, while tailored reward functions, reset conditions, and normalization criteria are designed to accelerate training convergence. Closed-loop stability of the overall control law is rigorously proven using Lyapunov methods. Simulation results demonstrate that the proposed controller reduces steady-state tracking errors by over 96% for tethers and 99% for node satellites, while cutting fuel consumption by two orders of magnitude compared with the baseline method. These results validate the effectiveness and stability of the proposed approach for TTFS deployment control.

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 8 of its 39 references that are in the map. First posted on arXiv 8 Jan 2026.

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  1. 2024Reinforcement learning16 May

Other references · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  6. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  7. 2020Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehiclescited by 11

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  5. 2026Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System ↗
  6. 2026High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning
  7. 2024Reinforcement Learning for Docking Maneuvers with Prescribed Performance
  8. 2025Stability Enhancement in Reinforcement Learning via Adaptive Control Lyapunov Function