arXiv subjects

Harry Holt

Publications and source records attributed to Harry Holt.

14 recordsLinked to original sources

arXiv2026

Deflective Sunshades: conceptual design and origami-inspired folding strategy

Space-based solar radiation management has been proposed as a direct means of modulating Earth's radiative balance by reducing incident solar flux. A prominent concept consists of large sunshades operating near the Sun-Earth L1 region. However, feasibility is constrained by a fundamental lower bound on the required mass, arising from the coupled requirements of achieving sufficient insolation reduction while maintaining dynamical equilibrium under solar radiation pressure (SRP). We introduce a class of sunshades, termed deflective, designed to shape the effective SRP through macroscopic surface geometry. By employing inclined reflective elements, these systems redirect incident radiation to simultaneously control flux attenuation and the resulting momentum exchange, enabling the use of conventional high-reflectivity materials (e.g., aluminium films). The concept admits multiple geometrical realizations, including conical, pyramidal, and louvered (venetian-blind) configurations. A conical configuration is then studied in more detail as a representative implementation of this broader design principle. To improve its packaging efficiency and scalability, we consider a distributed constellation of units and propose a flat-folding strategy based on a Miura-Ori pattern adapted to the conical geometry finding its optimal configuration parameters resulting in a favourable geometry.

physics.space-ph↗

arXiv2026

Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning

This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed-loop correction law comprising a feedforward control adjustment and time-varying feedback gains. Probabilistic feasibility is enforced empirically through rollout-based upper-tail quantiles, while terminal dispersion is regulated through covariance-feasibility penalties. The framework is assessed on two materially different trajectory design problems. The flagship case study is a three-dimensional multi-impulse Earth-Mars transfer, where the learned policy is benchmarked against a recent robust trajectory-optimization reference under Gaussian uncertainty and then evaluated under bounded uniform uncertainty and under process disturbances not seen during training. The second case study is a stochastic atmospheric pinpoint rocket landing problem, used to assess portability to a short-horizon continuous-thrust setting with drag, mass depletion, and glide-slope constraints. The results show that the proposed framework can remain competitive in upper-tail fuel cost while preserving probabilistic feasibility, and that the same robustification scaffold can be carried across heterogeneous spacecraft trajectory planning problems without redesign of its core stochastic-control structure.

math.OC↗

arXiv2026

A practical guide to implementing zero-order-hold interplanetary trajectory legs

We study the practical implementation of zero-order-hold (ZOH) transcriptions for spacecraft trajectory optimisation, identifying a set of design principles that render them robust across a broad class of dynamical settings without problem-specific tuning. The contributions are fourfold: (i) a thorough study of the forward--backward shooting construction, denoted $\mathrm{ZOH}_\alpha$; (ii) a redundant four-dimensional throttle parameterization that eliminates the singularity of the control influence matrix along ballistic arcs; (iii) a softmax time-grid encoding that avoids ordering constraints on segment durations while preserving full differentiability; and (iv) the TOPS benchmark (Trajectory Optimisation Problems in Space), a suite of 28 problems spanning four dynamical models, two-body Cartesian, modified equinoctial elements, circular restricted three-body, and solar sailing, designed to be extended over time.

astro-ph.IM↗

arXiv2025

Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks

Guidance & control networks (G&CNETs) provide a promising alternative to on-board guidance and control (G&C) architectures for spacecraft, offering a differentiable, end-to-end representation of the guidance and control architecture. When training G&CNETs, two predominant paradigms emerge: behavioural cloning (BC), which mimics optimal trajectories, and reinforcement learning (RL), which learns optimal behaviour through trials and errors. Although both approaches have been adopted in G&CNET related literature, direct comparisons are notably absent. To address this, we conduct a systematic evaluation of BC and RL specifically for training G&CNETs on continuous-thrust spacecraft trajectory optimisation tasks. We introduce a novel RL training framework tailored to G&CNETs, incorporating decoupled action and control frequencies alongside reward redistribution strategies to stabilise training and to provide a fair comparison. Our results show that BC-trained G&CNETs excel at closely replicating expert policy behaviour, and thus the optimal control structure of a deterministic environment, but can be negatively constrained by the quality and coverage of the training dataset. In contrast RL-trained G&CNETs, beyond demonstrating a superior adaptability to stochastic conditions, can also discover solutions that improve upon suboptimal expert demonstrations, sometimes revealing globally optimal strategies that eluded the generation of training samples.

eess.SY↗

arXiv2025

Using the Translation Theorem for the Automated Stationkeeping of Extremely-Low Lunar Missions

Extremely-Low Lunar Orbits (eLLOs) (altitudes $\leq 50$ km) exhibit severe perturbations due to the highly non-spherical lunar gravitational field, presenting unique challenges to orbit maintenance. These altitudes are too low for the existence of stable `frozen' orbits, and naive stationkeeping methods, such as circularization, perform poorly. However, mission designers have noticed a particular characteristic of low lunar orbits, which they have found useful for stationkeeping and dubbed the "translation theorem", wherein the eccentricity vector follows a predictable monthly pattern that is independent of its starting value. We demonstrate this feature results from the low orbital eccentricity combined with the dominant effect of a particular subset of sectoral and tesseral harmonics. Subsequently, automated stationkeeping strategies for eLLOs are presented, utilizing this theorem for eccentricity vector control. Several constraints within the eccentricity vector plane are explored, including circular, annular, and elevation-model derived regions, each forming distinct stationkeeping strategies for varying orbital configurations. Subsequently, the optimal control profiles for these maneuvers within the eccentricity plane are obtained using Sequential Convex Programming (SCP). The proposed strategies offer computational simplicity and clear advantages when compared to traditional methods and are comparable to full trajectory optimization.

astro-ph.IM↗

arXiv2025

Learning-Based Stable Optimal Guidance for Spacecraft Close-Proximity Operations

Machine learning techniques have demonstrated their effectiveness in achieving autonomy and optimality for nonlinear and high-dimensional dynamical systems. However, traditional black-box machine learning methods often lack formal stability guarantees, which are critical for safety-sensitive aerospace applications. This paper proposes a comprehensive framework that combines control Lyapunov functions with supervised learning to provide certifiably stable, time- and fuel-optimal guidance for rendezvous maneuvers governed by Clohessy-Wiltshire dynamics. The framework is easily extensible to nonlinear control-affine systems. A novel neural candidate Lyapunov function is developed to ensure positive definiteness. Subsequently, a control policy is defined, in which the thrust direction vector minimizes the Lyapunov function's time derivative, and the thrust throttle is determined using minimal required throttle. This approach ensures that all loss terms related to the control Lyapunov function are either naturally satisfied or replaced by the derived control policy. To jointly supervise the Lyapunov function and the control policy, a simple loss function is introduced, leveraging optimal state-control pairs obtained by a polynomial maps based method. Consequently, the trained neural network not only certifies the Lyapunov function but also generates a near-optimal guidance policy, even for the bang-bang fuel-optimal problem. Extensive numerical simulations are presented to validate the proposed method.

eess.SY↗

arXiv2024

Multi-Target Spacecraft Mission Design using Convex Optimization and Binary Integer Programming

The optimal design of multi-target rendezvous and flyby missions is challenging due to the combination of traditional spacecraft trajectory optimization and high-dimensional combinatorial problems. This often requires large-scale global search techniques or simplified approximations that rely on manual tuning to be performant. While global search techniques are typically computationally expensive, limiting their use in time- or cost-constrained scenarios, this work proposes a computationally efficient nested-loop approach. The problem is split into separate combinatorial and optimal control subproblems: the combinatorial problem is solved using Binary Integer Programming (BIP) with a fixed rendezvous time schedule, while the optimal control problem is handled with adaptive-mesh Sequential Convex Programming (SCP), which also optimizes the time schedule. By iterating these processes in a nested-loop structure, the approach can efficiently find high-quality solutions. When, applied to the Global Trajectory Optimization Competition 12 (GTOC 12) problem, this method results in several new best-known solutions.

astro-ph.IM↗

arXiv2024

GTOC 12: Results from TheAntipodes

We present the solution approach developed by the team `TheAntipodes' during the 12th edition of the Global Trajectory Optimization Competition (GTOC 12). An overview of the approach is as follows: (1) generate asteroid subsets, (2) chain building with beam search, (3) convex low-thrust trajectory optimization, (4) manual refinement of rendezvous times, and (5) optimal solution set selection. The generation of asteroid subsets involves a heuristic process to find sets of asteroids that are likely to permit high-scoring asteroid chains. Asteroid sequences `chains' are built within each subset through a beam search based on Lambert transfers. Low-thrust trajectory optimization involves the use of sequential convex programming (SCP), where a specialized formulation finds the mass-optimal control for each ship's trajectory within seconds. Once a feasible trajectory has been found, the rendezvous times are manually refined with the aid of the control profile from the optimal solution. Each ship's individual solution is then placed into a pool where the feasible set that maximizes the final score is extracted using a genetic algorithm. Our final submitted solution placed fifth with a score of $15,489$.

astro-ph.IM↗

arXiv2024

Trajectory Design and Guidance for Far-range Proximity Operations of Active Debris Removal Missions with Angles-only Navigation and Safety Considerations

Observability of the target, safety, and robustness are often recognized as critical factors in ensuring successful far-range proximity operations. The application of angles-only (AO) navigation for proximity operations is often met with hesitancy due to its inherent limitations in determining range, leading to issues in target observability and consequently, mission safety. However, this form of navigation remains highly appealing due to its low cost. This work employs Particle Swarm Optimization (PSO) and Reinforcement Learning (RL) for the design and guidance of such far-range trajectories, assuring observability, safety and robustness under angles-only navigation. Firstly, PSO is used to design a nominal trajectory that is observable, robust and safe. Subsequently, Proximal Policy Optimization (PPO), a cutting-edge RL algorithm, is utilized to develop a guidance controller capable of maintaining observability while steering the spacecraft from an initial perturbed state to a target state. The fidelity of the guidance controller is then tested in a Monte-Carlo (MC) manner by varying the initial relative spacecraft state. The observability of the nominal trajectory and the perturbed trajectories with guidance are validated using an Extended Kalman Filter (EKF). The perturbed trajectories are also shown to adhere to the safety requirements satisfied by the nominal trajectory. Results demonstrate that the trained controller successfully determines maneuvers that maintain observability and safety and reaches the target end state.

math.OC↗

arXiv2024

Certifying Guidance & Control Networks: Uncertainty Propagation to an Event Manifold

We perform uncertainty propagation on an event manifold for Guidance & Control Networks (G&CNETs), aiming to enhance the certification tools for neural networks in this field. This work utilizes three previously solved optimal control problems with varying levels of dynamics nonlinearity and event manifold complexity. The G&CNETs are trained to represent the optimal control policies of a time-optimal interplanetary transfer, a mass-optimal landing on an asteroid and energy-optimal drone racing, respectively. For each of these problems, we describe analytically the terminal conditions on an event manifold with respect to initial state uncertainties. Crucially, this expansion does not depend on time but solely on the initial conditions of the system, thereby making it possible to study the robustness of the G&CNET at any specific stage of a mission defined by the event manifold. Once this analytical expression is found, we provide confidence bounds by applying the Cauchy-Hadamard theorem and perform uncertainty propagation using moment generating functions. While Monte Carlo-based (MC) methods can yield the results we present, this work is driven by the recognition that MC simulations alone may be insufficient for future certification of neural networks in guidance and control applications.

eess.SY↗

arXiv2023

Convex Optimization-based Model Predictive Control for Active Space Debris Removal Mission Guidance

A convex optimization-based model predictive control (MPC) algorithm for the guidance of active debris removal (ADR) missions is proposed in this work. A high-accuracy reference for the convex optimization is obtained through a split-Edelbaum approach that takes the effects of J2, drag, and eclipses into account. When the spacecraft deviates significantly from the reference trajectory, a new reference is calculated through the same method to reach the target debris. When required, phasing is integrated into the transfer. During the mission, the phase of the spacecraft is adjusted to match that of the target debris at the end of the transfer by introducing intermediate waiting times. The robustness of the guidance scheme is tested in a high-fidelity dynamical model that includes thrust errors and misthrust events. The guidance algorithm performs well without requiring successive convex iterations. Monte-Carlo simulations are conducted to analyze the impact of these thrust uncertainties on the guidance. Simulation results show that the proposed convex-MPC approach can ensure that the spacecraft can reach its target despite significant uncertainties and long-duration misthrust events.

math.OC↗

arXiv2023

Convex Optimization-Based Model Predictive Control for the Guidance of Active Debris Removal Transfers

Active debris removal (ADR) missions have garnered significant interest as means of mitigating collision risks in space. This work proposes a convex optimization-based model predictive control (MPC) approach to provide guidance for such missions. While convex optimization can obtain optimal solutions in polynomial time, it relies on the successive convexification of nonconvex dynamics, leading to inaccuracies. Here, the need for successive convexification is eliminated by using near-linear Generalized Equinoctial Orbital Elements (GEqOE) and by updating the reference trajectory through a new split-Edelbaum approach. The solution accuracy is then measured relative to a high-fidelity dynamics model, showing that the MPC-convex method can generate accurate solutions without iterations.

math.OC↗

arXiv2022

Design and Guidance of a Multi-Active Debris Removal Mission

Space debris have been becoming exceedingly dangerous over the years as the number of objects in orbit continues to rise. Active debris removal (ADR) missions have garnered significant attention as an effective way to mitigate this collision risk. This research focuses on developing a multi-ADR mission that utilizes controlled reentry and deorbiting. The mission comprises two spacecraft: a Servicer that brings debris down to a low altitude and a Shepherd that rendezvous with the debris to later perform a controlled reentry. A preliminary mission design tool (PMDT) is developed to obtain time or fuel optimal trajectories for the proposed mission while taking the effect of $J_2$, drag, eclipses, and duty ratio into account. The PMDT can perform such trajectory optimizations within computational times that are under a minute. Three guidance schemes are also studied, taking the PMDT solution as a reference, to validate the design methodology and provide guidance solutions for this complex mission profile.

math.DS↗

arXiv2022

Team theAntipodes: Solution Methodology for GTOC11

This paper presents the solution approach developed by the team "theAntipodes" for the 11th Global Trajectory Optimization Competition (GTOC11). The approach consists of four main blocks: 1) mothership chain generation, 2) rendezvous table generation, 3) the dispatcher, and 4) the refinement. Blocks 1 and 3 are purely combinatorial optimization problems that select the asteroids to visit and allocate them to the Dyson ring stations. The rendezvous table generation involves interpolating time-optimal transfers to find all transfer opportunities between selected asteroids and the ring stations. The dispatcher uses the data stored in the table and allocates the asteroids to the Dyson ring stations optimally. The refinement ensures each rendezvous trajectory meets the problem accuracy constraints and introduces deep-space maneuvers to the mothership transfers. We provide the details of our solution that, with a score of 5,992, was worth 3rd place.

math.OC↗