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

Theofania Karampela, Rishie Seshadri, Florian Dörfler, Sarah H. Q. Li

TL;DR

The paper develops a nonlinear model predictive control framework to enable a two-module servicer satellite to achieve zero-impulse contact with a free-spinning target in orbit, explicitly capturing momentum coupling between a moment-generation base with a 3-DoF RW cluster and a 3-DoF manipulation arm. By deriving a reduced-state, nonlinearly coupled dynamics model and solving two phase-specific MPC problems (spin synchronization and zero-impulse contact) with acados, the approach enforces actuation and state constraints that prior methods neglect. Monte Carlo simulations demonstrate that the MPC controller significantly reduces constraint violations and tracking errors compared to a PID baseline, particularly under time-varying trajectories and noisy conditions, albeit with higher computation time per cycle. The work highlights the importance of momentum-aware planning for autonomous on-orbit servicing and points to future directions in decomposition and hardware validation to enable real-time onboard implementation.

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.

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

TL;DR

The paper develops a nonlinear model predictive control framework to enable a two-module servicer satellite to achieve zero-impulse contact with a free-spinning target in orbit, explicitly capturing momentum coupling between a moment-generation base with a 3-DoF RW cluster and a 3-DoF manipulation arm. By deriving a reduced-state, nonlinearly coupled dynamics model and solving two phase-specific MPC problems (spin synchronization and zero-impulse contact) with acados, the approach enforces actuation and state constraints that prior methods neglect. Monte Carlo simulations demonstrate that the MPC controller significantly reduces constraint violations and tracking errors compared to a PID baseline, particularly under time-varying trajectories and noisy conditions, albeit with higher computation time per cycle. The work highlights the importance of momentum-aware planning for autonomous on-orbit servicing and points to future directions in decomposition and hardware validation to enable real-time onboard implementation.

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.

Paper Structure

This paper contains 22 sections, 54 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Servicer satellite dynamics modeling
  4. Moment-generation module: 3-DoF RW cluster and cuboid base
  5. Manipulation module: 3-DoF manipulation arm
  6. Momentum-coupled dynamics derivation
  7. MPC framework
  8. Spin synchronization MPC
  9. Zero-impulse contact MPC
  10. Simulation and results
  11. Environment setup
  12. Reference trajectory definition
  13. PID controller design and tuning
  14. Case study set up and performance metrics
  15. Results and discussion
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Servicer satellite with a $3$-DoF manipulation arm and a moment-generation base.
  • Figure 2: Case study A results. Phase A (first column): servicer satellite angular velocity two-norm error (top), relative orientation quaternion two-norm error (middle), and RW torque infinity norm (bottom) versus operation horizon $\mathcal{T}_A$. Phase B (second column): joint angle two-norm error (top), servicer satellite angular velocity two-norm error (middle), and RW torque infinity norm (bottom) versus operation horizon $\mathcal{T}_B$. Phase B (third column): joint velocity two-norm error (top), relative orientation quaternion two-norm error (middle), joint torque infinity norm (bottom) versus versus operation horizon $\mathcal{T}_B$.
  • Figure 3: Case study B results. Phase A (first column): servicer satellite angular velocity two-norm error (top), relative orientation quaternion two-norm error (middle), RW torque infinity norm (bottom) versus operation horizon $\mathcal{T}_A$. Phase B (second column): joint angle two-norm error (top), servicer satellite angular velocity two-norm error (middle), RW torque infinity norm (bottom) vs operation horizon $\mathcal{T}_B$. Phase B (third column): joint velocity two-norm error (top), relative orientation quaternion two-norm error (middle), joint torque infinity norm (bottom) versus operation horizon $\mathcal{T}_B$.
  • Figure 4: Case study C results. Phase A (first column): servicer satellite angular velocity two-norm error (top), relative orientation quaternion two-norm error (middle), RW torque infinity norm (bottom) versus operation horizon $\mathcal{T}_A$. Phase B (second column): joint angle two-norm error (top), servicer satellite angular velocity two-norm error (middle), RW torque infinity norm (bottom) vs operation horizon $\mathcal{T}_B$. Phase B (third column): joint velocity two-norm error (top), relative orientation quaternion two-norm error (middle), joint torque infinity norm (bottom) versus operation horizon $\mathcal{T}_B$.