Mixed-Integer vs. Continuous Model Predictive Control for Binary Thrusters: A Comparative Study

• URL: http://arxiv.org/abs/2603.19796v2
• Date: Mon, 23 Mar 2026 08:22:54 GMT
• Title: Mixed-Integer vs. Continuous Model Predictive Control for Binary Thrusters: A Comparative Study
• Abstract summary: 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.
• Score: 0.22940141855172033
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Binary on/off thrusters are commonly used for spacecraft attitude and position control during proximity operations. However, their discrete nature poses challenges for conventional continuous control methods. The control of these discrete actuators is either explicitly formulated as a mixed-integer optimization problem or handled in a two-layer approach, where a continuous controller's output is converted to binary commands using analog-to digital modulation techniques such as Delta-Sigma-modulation. This paper provides the first systematic comparison between these two paradigms for binary thruster control, contrasting continuous Model Predictive Control (MPC) with Delta-Sigma modulation against direct Mixed-Integer MPC (MIMPC) approaches. Furthermore, we propose a new variant of MPC for binary actuated systems, which is informed using the state of the Delta-Sigma Modulator. The two variations for the continuous MPC along with the MIMPC are evaluated through extensive simulations using ESA's REACSA platform. Results demonstrate that while all approaches perform similarly in high-thrust regimes, MIMPC achieves superior fuel efficiency in low-thrust conditions. Continuous MPC with modulation shows instabilities at higher thrust levels, while binary informed MPC, which incorporates modulator dynamics, improves robustness and reduces the efficiency gap to the MIMPC. It can be seen from the simulated and real-system experiments that MIMPC offers complete stability and fuel efficiency benefits, particularly for resource-constrained missions, while continuous control methods remain attractive for computationally limited applications.

Related papers

• Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control [0.5599792629509229]
  Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation.<n>This paper investigates neural-network-based control strategies for a fully actuated tiltrotor system with four thrust-vectoring inputs.
  arXiv  Detail & Related papers  (2026-06-07T16:10:46Z)
• Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control [51.92884966472683]
  MSCoT is a multi-scale, coarse-to-fine model for test-time human motion synthesis and control.<n> MSCoT discretizes motion into a multi-scale hierarchical representation and predicts the entire token sequence at each temporal scale.
  arXiv  Detail & Related papers  (2026-05-14T15:09:49Z)
• Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine [0.0]
  This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine.<n>A physics-based component-level model is developed to generate training data and validate the controllers.<n>Two controllers are evaluated for two control strategies, namely configurations of spool speeds and EPR, under both sea-level and varying flight conditions.
  arXiv  Detail & Related papers  (2026-04-02T07:51:59Z)
• 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)
• MPC for momentum counter-balanced and zero-impulse contact with a free-spinning satellite [4.654827623279546]
  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.
  arXiv  Detail & Related papers  (2025-12-10T00:53:10Z)
• EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation [0.0]
  Control theory emphasizes the use of state space models (SSMs) for managing long-range dependencies. We introduce EchoMamba4Rec, a model for predicting user preferences and sequential dependencies based on historical behavior. We show that EchoMamba significantly outperforms existing models, providing more accurate and personalized recommendations.
  arXiv  Detail & Related papers  (2024-06-04T09:07:58Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• Generation of C-NOT, SWAP, and C-Z Gates for Two Qubits Using Coherent and Incoherent Controls and Stochastic Optimization [56.47577824219207]
  We consider a general form of the dynamics of open quantum systems determined by the Gorini-Kossakowsky-Sudarchhan-Lindblad type master equation. We analyze the control problems of generating two-qubit C-NOT, SWAP, and C-Z gates using piecewise constant controls and optimization.
  arXiv  Detail & Related papers  (2023-12-09T17:55:47Z)
• Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics [64.72260320446158]
  We propose a method for synthesising controllers for Markov jump linear systems. Our method is based on a finite-state abstraction that captures both the discrete (mode-jumping) and continuous (stochastic linear) behaviour of the MJLS. We apply our method to multiple realistic benchmark problems, in particular, a temperature control and an aerial vehicle delivery problem.
  arXiv  Detail & Related papers  (2022-12-01T17:36:30Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• An Artificial Neural Network-Based Model Predictive Control for Three-phase Flying Capacitor Multi-Level Inverter [2.3513645401551333]
  Model predictive control (MPC) has been used widely in power electronics due to its simple concept, fast dynamic response, and good reference tracking. It suffers from parametric uncertainties, since it relies on the mathematical model of the system to predict the optimal switching states. This paper offers a model-free control strategy on the basis of artificial neural networks (ANNs)
  arXiv  Detail & Related papers  (2021-10-15T13:54:08Z)
• 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.