MPC for momentum counter-balanced and zero-impulse contact with a free-spinning satellite

• URL: http://arxiv.org/abs/2512.09213v1
• Date: Wed, 10 Dec 2025 00:53:10 GMT
• Title: MPC for momentum counter-balanced and zero-impulse contact with a free-spinning satellite
• Abstract summary: A servicer satellite's ability to make contact with a free-spinning target satellite is essential to completing most on-orbit servicing tasks.<n>This manuscript develops a nonlinear model predictive control framework that generates feasible controls for a servicer satellite to achieve zero-impulse contact with a free-spinning target satellite.
• Score: 4.654827623279546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In on-orbit robotics, a servicer satellite's ability to make contact with a free-spinning target satellite is essential to completing most on-orbit servicing (OOS) tasks. This manuscript develops a nonlinear model predictive control (MPC) framework that generates feasible controls for a servicer satellite to achieve zero-impulse contact with a free-spinning target satellite. The overall maneuver requires coordination between two separately actuated modules of the servicer satellite: (1) a moment generation module and (2) a manipulation module. We apply MPC to control both modules by explicitly modeling the cross-coupling dynamics between them. We demonstrate that the MPC controller can enforce actuation and state constraints that prior control approaches could not account for. We evaluate the performance of the MPC controller by simulating zero-impulse contact scenarios with a free-spinning target satellite via numerical Monte Carlo (MC) trials and comparing the simulation results with prior control approaches. Our simulation results validate the effectiveness of the MPC controller in maintaining spin synchronization and zero-impulse contact under operation constraints, moving contact location, and observation and actuation noise.

Related papers

• Communication in modular robotic motor control: Bilateral controllers under realistic constraints [2.9376172882151366]
  We introduce a recurrent controller of two GRU-based modules connected by a learnable, delayed inter-hemispheric channel.<n>Compared to a matched modular controller without communication, learned inter-hemispheric communication reshapes the solution.<n>Our findings show that for robotics, biologically inspired modular controllers offer a practical route to robust movement under noise and energetic constraints.
  arXiv  Detail & Related papers  (2026-08-14T03:17:12Z)
• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation [85.15806404962193]
  Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation.<n>We evaluate ACWMs through the observable capabilities expected of physical simulators.<n>WorldSimProbe reveals systematic action-realization degradation across control variation, structured failures in interaction grounding and dynamics.
  arXiv  Detail & Related papers  (2026-08-10T08:48:06Z)
• Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks [53.12548325396181]
  Tendon drives paired with soft muscle actuation enable faster and safer robots.<n>We propose a sim-to-real pipeline that learns a neural network model of this complex actuation.<n>We successfully deploy precise goal-reaching and dynamic ball-in-a-cup policies trained entirely in simulation.
  arXiv  Detail & Related papers  (2026-04-10T16:52:54Z)
• Lightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization [39.146761527401424]
  This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization.<n>We develop a single-loop and a dual-loop MPC; the latter embeds a stabilizing feedback controller within the inner loop, yielding a linear time-invariant system.
  arXiv  Detail & Related papers  (2026-03-19T13:58:55Z)
• Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control [46.47619556874003]
  We propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning policy for legged locomotion.<n>The admittance controller maps external wrenches--such as those applied by a human during physical interaction--into desired end-effector velocities, allowing for compliant behavior.<n>We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor.
  arXiv  Detail & Related papers  (2026-03-02T22:31:12Z)
• Autonomous Manipulation of Hazardous Chemicals and Delicate Objects in a Self-Driving Laboratory: A Sliding Mode Approach [3.7215125870216013]
  Sliding Mode Control (SMC) has emerged as a robust approach for managing uncertainties and disturbances in manipulator dynamics.<n>This study implements a model-based SMC (MBSMC) utilizing a hyperbolic tangent function to regulate the motion of a manipulator mounted on a mobile platform.<n>Compared to PID and NMBSMC, MBSMC achieved significantly smoother motion and up to 90% lower control effort.
  arXiv  Detail & Related papers  (2026-01-18T21:05:47Z)
• A Sliding Mode Controller Based on Timoshenko Beam Theory Developed for a Tendon-Driven Robotic Wrist [3.5026278670865874]
  This paper presents the design and implementation of a tendon-driven robotic wrist joint together with an efficient Sliding Mode Controller (SMC) for precise motion control.<n>The wrist mechanism is modeled using a Timoshenko-based approach to accurately capture its kinematic and dynamic properties.<n>The effectiveness of the controller is validated through comparative analyses with existing controllers for similar wrist mechanisms.
  arXiv  Detail & Related papers  (2026-01-11T17:48:43Z)
• LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller [0.17398560678845076]
  We present the first successful in-orbit demonstration of an AI-based attitude controller for inertial pointing maneuvers.<n>The controller was trained entirely in simulation and deployed to the InnoCube 3U nanosatellite, which was developed by the Julius-Maximilians-Universitt Wrzburg in cooperation with the Technische Universitt Berlin.<n>We present the AI agent design, the methodology of the training procedure, the discrepancies between the simulation and the observed behavior of the real satellite, and a comparison of the AI-based attitude controller with the classical PD controller of InnoCube.
  arXiv  Detail & Related papers  (2025-12-22T17:00:25Z)
• RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking [50.200035833530876]
  RobotDancing is a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies.<n>It can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality.
  arXiv  Detail & Related papers  (2025-09-25T03:30:34Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• Interactive Character Control with Auto-Regressive Motion Diffusion Models [18.727066177880708]
  We propose A-MDM (Auto-regressive Motion Diffusion Model) for real-time motion synthesis. Our conditional diffusion model takes an initial pose as input, and auto-regressively generates successive motion frames conditioned on previous frame. We introduce a suite of techniques for incorporating interactive controls into A-MDM, such as task-oriented sampling, in-painting, and hierarchical reinforcement learning.
  arXiv  Detail & Related papers  (2023-06-01T07:48:34Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.