Model Reference Adaptive Control for Net-Based Debris Towing

Tether-net-based debris removal missions are deemed promising, but the postcapture control of net-enveloped targets by a chaser is understudied. In fact, existing studies on postcapture control are limited to open-loop thrust control or to feedback controllers designed for simplified models that do not consider the presence of a flexible net. To bridge this gap, advancements in closed-loop control algorithms are necessary for this critical phase. In this work, two tether elongation control algorithms are developed based on model reference adaptive control (MRAC) algorithms to stabilize the full coupled chaser-tether-net-debris system, with the flexible net completely modeled in simulation. These algorithms, named MRAC-1 and MRAC-2, are designed to modify the dynamics of the net-based system so that it behaves similarly to an ideal and stable reference model. The proposed MRAC-1 algorithm requires lower effort for initial parameter selection, while MRAC-2 benefits from greater adaptability. Compared to a proportional–integral–derivative control baseline, both MRAC algorithms demonstrate improved transient behavior in a nominal towing scenario and achieve convergence 68% faster. Results from 90 simulations with varying initial debris states, inertial properties, and imperfect measurements show that the proposed algorithms are generally robust across different conditions, enabling the net-based debris removal system to achieve the desired steady and safe behavior in the postcapture phase.

Authors

Institutions

Publication Details

Journal
Journal of Guidance Control and Dynamics
Published
2026-10-16
DOI
https://doi.org/10.2514/1.g010031
Primary Topic
Space Satellite Systems and Control
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Model Reference Adaptive Control for Net-Based Debris Towing

Eleonora Marina Botta, Achira Boonrath
Journal of Guidance Control and Dynamics
Space Satellite Systems and Control
article

Model Reference Adaptive Control for Net-Based Debris Towing

Eleonora Marina Botta, Achira Boonrath
article en

Abstract

Tether-net-based debris removal missions are deemed promising, but the postcapture control of net-enveloped targets by a chaser is understudied. In fact, existing studies on postcapture control are limited to open-loop thrust control or to feedback controllers designed for simplified models that do not consider the presence of a flexible net. To bridge this gap, advancements in closed-loop control algorithms are necessary for this critical phase. In this work, two tether elongation control algorithms are developed based on model reference adaptive control (MRAC) algorithms to stabilize the full coupled chaser-tether-net-debris system, with the flexible net completely modeled in simulation. These algorithms, named MRAC-1 and MRAC-2, are designed to modify the dynamics of the net-based system so that it behaves similarly to an ideal and stable reference model. The proposed MRAC-1 algorithm requires lower effort for initial parameter selection, while MRAC-2 benefits from greater adaptability. Compared to a proportional–integral–derivative control baseline, both MRAC algorithms demonstrate improved transient behavior in a nominal towing scenario and achieve convergence 68% faster. Results from 90 simulations with varying initial debris states, inertial properties, and imperfect measurements show that the proposed algorithms are generally robust across different conditions, enabling the net-based debris removal system to achieve the desired steady and safe behavior in the postcapture phase.

Journal of Guidance Control and Dynamics
University at Buffalo, State University of New York (US)
Openalex Percentile: Top 18%
Space Satellite Systems and Control
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.