Lightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization
- URL: http://arxiv.org/abs/2603.18921v1
- Date: Thu, 19 Mar 2026 13:58:55 GMT
- Title: Lightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization
- Abstract summary: 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.
- Score: 39.146761527401424
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviation formulation, which enables the use of inherently linear constraints on angular velocity. 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. Both controllers are implemented with CasADi - including automatic code generation - evaluated across various solvers, and validated within the Basilisk astrodynamics simulation framework. The experimental results demonstrate improved tracking accuracy alongside reductions in computational effort and memory consumption. Finally, embedded delivery to an ARM Cortex-M7 - representative of commercial off-the-shelf devices used in New Space platforms - confirms the real-time feasibility of these approaches and highlights their suitability for onboard attitude control in resource-constrained spacecraft rendezvous missions.
Related papers
- 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) - A Task-Space Receding Horizon Controller for Fast Collision Avoidance [0.08594140167290099]
Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion.<n>Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints.<n>We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference.
arXiv Detail & Related papers (2026-07-17T08:13:12Z) - KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility [27.73146983499444]
Deep Reinforcement Learning (DRL) has shown promise for social navigation, yet its real-world deployment remains hindered by a persistent sim-to-real gap.<n>This work presents a unified framework that addresses these limitations to produce dynamically feasible navigation policies.<n> Experiments in real-world environments demonstrate that, when combined with the proposed tracking pipeline, KinematicRL can be deployed on a real differential drive robot with minimal modifications.
arXiv Detail & Related papers (2026-06-10T13:07:03Z) - Mixed-Integer vs. Continuous Model Predictive Control for Binary Thrusters: A Comparative Study [0.22940141855172033]
Binary on/off thrusters are commonly used for spacecraft attitude and position control during proximity operations.<n>The control of these discrete actuators is either explicitly formulated as a mixed-integer optimization problem or handled in a two-layer approach.<n>This paper provides the first systematic comparison between these two paradigms for binary thruster control.
arXiv Detail & Related papers (2026-03-20T09:37:26Z) - Dual Quaternion Based Contact Modeling for Fast and Smooth Collision Recovery of Quadrotors [11.70412524968217]
This letter presents a dual quaternion reset map that resolves rigid-body impacts directly on the SE(3) manifold.<n>A hybrid recovery controller is designed that couples linear and angular momentum to ensure strict energy dissipation across impacts.<n>High-fidelity MuJoCo simulations validate the controller's response to complex contact dynamics.
arXiv Detail & Related papers (2026-03-16T01:10:04Z) - Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
arXiv Detail & Related papers (2026-02-17T15:10:44Z) - Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration [8.727025636218528]
Existing spacecraft rendezvous and docking control methods rely on predefined dynamic models.<n>This paper proposes an Imitation Learning-based framework that learns control policies from expert demonstrations.<n>The proposed framework achieves accurate and energy-efficient model-free rendezvous and docking control.
arXiv Detail & Related papers (2026-01-19T10:58:26Z) - PreGME: Prescribed Performance Control of Aerial Manipulators based on Variable-Gain ESO [12.679195185695164]
We propose a novel prescribed performance motion control framework based on variable-gain extended state observers (ESOs)<n>The method includes variable-gain ESOs for real-time estimation of dynamic coupling and a prescribed performance flight control.<n> Experimental results demonstrate that even under the dynamic coupling caused by rapid robotic arm motion, the proposed method achieves high tracking performance.
arXiv Detail & Related papers (2025-12-28T14:54:06Z) - Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems [0.0]
We present an explainable AI-enhanced supervisory control framework for multi-agent robotics.<n>We validated the approach in two contrasting domains, spacecraft formation flying and autonomous underwater vehicles.
arXiv Detail & Related papers (2025-09-18T23:59:13Z) - 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) - Deep Bilinear Koopman Model for Real-Time Vehicle Control in Frenet Frame [0.0]
This paper presents a deep Koopman approach for modeling and control of vehicle dynamics within the curvilinear Frenet frame.<n>The proposed framework uses a deep neural network architecture to simultaneously learn the Koopman operator and its associated invariant subspace from the data.<n>The proposed controller achieved significant reductions in tracking error relative to baseline controllers, confirming its suitability for real-time implementation in embedded autonomous vehicle systems.
arXiv Detail & Related papers (2025-07-16T18:49:44Z) - Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model [63.336123527432136]
We introduce Bench2Drive-R, a generative framework that enables reactive closed-loop evaluation.<n>Unlike existing video generative models for autonomous driving, the proposed designs are tailored for interactive simulation.<n>We compare the generation quality of Bench2Drive-R with existing generative models and achieve state-of-the-art performance.
arXiv Detail & Related papers (2024-12-11T06:35:18Z) - Automatic dimensionality reduction of Twin-in-the-Loop Observers [1.440041584757981]
This paper focuses on developing a procedure to reduce the observer's complexity by exploring both supervised and unsupervised learning approaches.<n>The effectiveness of these strategies is validated for longitudinal and lateral vehicle dynamics using real-world data.
arXiv Detail & Related papers (2024-01-18T10:14:21Z) - A Tricycle Model to Accurately Control an Autonomous Racecar with Locked
Differential [71.53284767149685]
We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar.
We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
arXiv Detail & Related papers (2023-12-22T16:29:55Z) - Designing a Robust Low-Level Agnostic Controller for a Quadrotor with
Actor-Critic Reinforcement Learning [0.38073142980732994]
We introduce domain randomization during the training phase of a low-level waypoint guidance controller based on Soft Actor-Critic.
We show that, by introducing a certain degree of uncertainty in quadrotor dynamics during training, we can obtain a controller that is capable to perform the proposed task using a larger variation of quadrotor parameters.
arXiv Detail & Related papers (2022-10-06T14:58:19Z) - 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) - Gaussian Process-based Min-norm Stabilizing Controller for
Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
We propose a novel compound kernel that captures the control-affine nature of the problem.
We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
arXiv Detail & Related papers (2020-11-14T01:27:32Z)
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.