arXiv subjects

Kenshiro Oguri

Publications and source records attributed to Kenshiro Oguri.

18 recordsLinked to original sources

arXiv2026

Global Optimization Framework for Automated Low-Thrust Gravity-Assist Trajectory Design

Gravity-assist trajectory design requires searching numerous leg combinations, but applying nonlinear programming (NLP) for low-thrust trajectory optimization to all combinations is computationally prohibitive. Therefore, conventional frameworks first perform broad search using lightweight trajectory models such as Lambert-based calculation and simplified deep-space maneuver models, while pruning infeasible candidates. However, to maintain efficiency, conventional approaches handle only low-dimensional formulations with limited constraints such as up to a couple of impulse maneuvers per leg. Consequently, they cannot accommodate the constrained multivariable optimization required for low-thrust design, running the risk of prematurely pruning viable trajectories. To address this, this paper introduces a convex pruning approach capable of solving constrained multivariable problems directly within broad search while retaining the computational efficiency. We then augment the broad search stage with a robust local low-thrust optimization algorithm based on thrust regularization, which together enable global exploration and optimization in an automated fashion. We apply the proposed framework in a BepiColombo-inspired scenario involving nine gravity assists, successfully demonstrating its broad search capability to discover a far greater number of solutions (36,335) compared to a conventional approach (4,664) while identifying a wider feasible launch window by one year.

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arXiv2026

Generalizing Output-Feedback Covariance Steering to Incorporate Non-Orthogonal Estimation Errors

This paper addresses the problem of steering a state distribution over a finite horizon in discrete time with output feedback. The incorporation of output feedback introduces additional challenges arising from the statistical coupling between the true state distribution and the corresponding filtered state distribution. In particular, this paper extends existing covariance steering formulations to scenarios in which estimation errors are not orthogonal to the state estimates. This paper presents a sequential convex programming framework with rank constraints to solve this more general covariance steering with output feedback problem. The proposed approach is validated through numerical examples and Monte Carlo simulations, including cases with non-orthogonal estimation errors that prior techniques cannot address.

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arXiv2026

On the Optimality of Markovian Policies for Chance-Constrained Covariance Steering

Many studies on finite-horizon stochastic optimal control, including covariance steering, parameterize control policies as state-history-affine. This parameterization enables a convex reformulation, thereby yielding a tractable solution method. However, the necessity of dependence on previous states has not been well established. \textit{Is this dependence necessary, or merely an artifact of the convex reformulation?} We show that it is an artifact that can be removed losslessly. Given an optimal solution of the state-history-affine formulation, we construct a deterministic Markovian policy which is affine in the current state. We show that, even for the covariance steering problem with a broad class of commonly used state and control safety constraints, the synthesized Markovian policy almost surely produces the same control actions as the history-dependent policy and therefore the same state trajectories, cost, and moments. Thus, every optimum of the history-dependent formulation admits a lossless Markovian transformation. Geometrically, the history-dependent formulation lifts the policy space for convexity, and its optimal solution can be projected back to the Markovian policy space. We extend the analysis to output feedback and a convex upper-bounding surrogate for value-at-risk costs.

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arXiv2026

Pontryagin-Bellman Differential Dynamic Programming for Low-Thrust Trajectory Optimization with Path Constraints

We present a Differential Dynamic Programming framework that parameterizes the control via Pontryagin's Minimum Principle, for constrained low-thrust trajectory optimization. This approach, dubbed Pontryagin-Bellman Differential Dynamic Programming (PDDP), optimizes the costates using a null-space trust-region method, solving a series of quadratic subproblems derived from first- and second-order sensitivities. Terminal equality constraints are handled via a general augmented Lagrangian method, while continuous-time state-path constraints are enforced using a quadratic penalty approach. The resulting solution method represents a significant improvement over classical indirect methods, which are known to face challenges for state-constrained problems. The flexibility of PDDP in handling various state-path constraints is demonstrated through minimum-radius and eclipse-avoidance trajectories in cislunar space. Finally, a trade study against indirect multiple shooting shows that PDDP achieves higher convergence rates overall, indicating improved robustness to poor initial guesses.

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arXiv2026

Chance-Constrained Covariance Steering for Discrete-Time Markov Jump Linear Systems

In this paper, we solve the chance-constrained covariance steering problem for discrete-time Markov Jump Linear Systems (MJLS) using a convex optimization framework. We derive the analytical expressions for the mean and covariance trajectories of time-varying discrete-time MJLS and show that they cannot be separated even without chance constraints, unlike the single-mode dynamics case. To solve the covariance steering problem, we propose a two-step convex optimization framework, which optimizes the mean and covariance subproblems sequentially. Further, we incorporate chance constraints and propose an iterative optimization framework to solve the chance-constrained covariance steering problem. Both problems are originally nonconvex, and we derive convex relaxations which are proved to be lossless at optimality using the Karush-Kuhn-Tucker (KKT) conditions. Numerical simulations demonstrate the proposed method by achieving target covariances while respecting chance constraints under additive noise, bias, and Markovian jump dynamics.

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arXiv2026

Convex Bound of Nonlinear Dynamical Errors for Covariance Steering

Applying linear controllers to nonlinear systems requires the dynamical linearization about a reference. In highly nonlinear environments such as cislunar space, the region of validity for these linearizations varies widely and can negatively affect controller performance if not carefully formulated. This paper presents a formulation that minimizes the nonlinear errors experienced by linear covariance controllers. The formulation involves upper-bounding the remainder term from the linearization process using higher-order terms in a Taylor series expansion, and resolving it into a convex function. This can serve as a cost function for controller gain optimization, and its convex nature allows for efficient solutions through convex optimization. This formulation is then demonstrated and compared with the current methods within a halo orbit stationkeeping scenario. The results show that the formulation proposed in this paper maintains the Gaussianity of the distribution in nonlinear simulations more effectively, thereby allowing the linear covariance controller to perform more as intended in nonlinear environments.

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arXiv2026

Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems

Accurate representation of non-Gaussian distributions of quantities of interest in nonlinear dynamical systems is critical for estimation, control, and decision-making, but can be challenging when forward propagations are expensive to carry out. This paper presents an approach for estimating probability density functions of states evolving under nonlinear dynamics using Seminonparametric (SNP), or Gallant-Nychka, densities. SNP densities employ a probabilists' Hermite polynomial basis to model non-Gaussian behavior and are positive everywhere on the support by construction. We use Monte Carlo to approximate the expectation integrals that arise in the maximum likelihood estimation of SNP coefficients, and introduce a convex relaxation to generate effective initial estimates. The method is demonstrated on density and quantile estimation for the chaotic Lorenz system. The results demonstrate that the proposed method can accurately capture non-Gaussian density structure and compute quantiles using significantly fewer samples than raw Monte Carlo sampling.

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arXiv2026

Convex Block-Cholesky Approach to Risk-Constrained Low-thrust Trajectory Design under Operational Uncertainty

Designing robust trajectories under uncertainties is an emerging technology that may represent a key paradigm shift in space mission design. As we pursue more ambitious scientific goals (e.g., multi-moon tours, missions with extensive components of autonomy), it becomes more crucial that missions are designed with navigation (Nav) processes in mind. The effect of Nav processes is statistical by nature, as they consist of orbit determination (OD) and flight-path control (FPC). Thus, this mission design paradigm calls for techniques that appropriately quantify statistical effects of Nav, evaluate associated risks, and design missions that ensure sufficiently low risk while minimizing a statistical performance metric; a common metric is Delta-V99: worst-case (99%-quantile) Delta-V expenditure including statistical FPC efforts. In response to the need, this paper develops an algorithm for risk-constrained trajectory optimization under operational uncertainties due to initial state dispersion, navigation error, maneuver execution error, and imperfect dynamics modeling. We formulate it as a nonlinear stochastic optimal control problem and develop a computationally tractable algorithm that combines optimal covariance steering and sequential convex programming (SCP). Specifically, the proposed algorithm takes a block-Cholesky approach for convex formulation of optimal covariance steering, and leverages a recent SCP algorithm, SCvx*, for reliable numerical convergence. We apply the developed algorithm to risk-constrained, statistical trajectory optimization for exploration of dwarf planet Ceres with a Mars gravity assist, and demonstrate the robustness of the statistically-optimal trajectory and FPC policies via nonlinear Monte Carlo simulation.

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arXiv2026

Square Root-Factorized Covariance Steering

Covariance steering (CS) synthesizes a control policy which drives the state's mean and covariance matrix towards desired values. Offering tractable computation of a closed-loop policy which can obey chance constraints in uncertain environments, application to many real-world control problems have been proposed. We consider the chance-constrained, discrete-time, linear time-varying CS with Gaussian noise. The contribution of this paper is a novel solution method for this problem, explicitly writing the propagation equations of the Cholesky factor of the state covariance matrix by using the QR decomposition. The use of the square-root form of covariance matrices brings two key benefits over other existing methods: (i) computational scalability and (ii) numerical reliability. (i) Compared to solution methods that require large block matrix formulations, the proposed method scales better with the growth in horizon length, shows better optimality, and uses memoryless state feedback. (ii) Compared to another class of methods that explicitly define the covariance matrix as variables, the proposed method allows flexible cost formulations and shows better numerical reliability when uncertainty terms are smaller than the mean. On the other hand, these benefits come with a minor drawback: the propagation equation of covariance square roots is non-convex, necessitating sequential convex programming to solve. However, this paper proves the global optimality of the proposed approach for CS without chance constraints. When chance constraints are present, the existing optimal CS formulation is also non-convex, and we prove that the proposed approach shares the same local minima. We verify the mathematical arguments via extensive numerical simulations.

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arXiv2025

Autonomous Horizon-based Asteroid Navigation With Observability-constrained Maneuvers

Small body exploration is a pertinent challenge due to low gravity environments and strong sensitivity to perturbations like Solar Radiation Pressure (SRP). Thus, autonomous methods are being developed to enable safe navigation and control around small bodies. These methods often involve using Optical Navigation (OpNav) to determine the spacecraft's location. Ensuring OpNav reliability would allow the spacecraft to maintain an accurate state estimate throughout its mission. This research presents an observability-constrained Lyapunov controller that steers a spacecraft to a desired target orbit while guaranteeing continuous OpNav observability. We design observability path constraints to avoid regions where horizon-based OpNav methods exhibit poor performance, ensuring control input that maintains good observability. This controller is implemented with a framework that simulates small body dynamics, synthetic image generation, edge detection, horizon-based OpNav, and filtering. We evaluate the approach in two representative scenarios, orbit maintenance and approach with circularization, around spherical and ellipsoidal target bodies. In Monte Carlo simulations, the proposed approach improves the rate of attaining target orbits without observability violations by up to 94% compared to an unconstrained Lyapunov baseline, demonstrating improved robustness over conventional methods.

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arXiv2025

Non-Gaussian Distribution Steering in Nonlinear Dynamics with Conjugate Unscented Transformation

In highly nonlinear systems such as the ones commonly found in astrodynamics, Gaussian distributions generally evolve into non-Gaussian distributions. This paper introduces a method for effectively controlling non-Gaussian distributions in nonlinear environments using optimized linear feedback control. This paper utilizes Conjugate Unscented Transformation to quantify the higher-order statistical moments of non-Gaussian distributions. The formulation focuses on controlling and constraining the sigma points associated with the uncertainty quantification, which would thereby reflect the control of the entire distribution and constraints on the moments themselves. This paper develops an algorithm to solve this problem with sequential convex programming, and it is demonstrated through a two-body and three-body example. The examples show that individual moments can be directly controlled, and the moments are accurately approximated for non-Gaussian distributions throughout the controller's time horizon in nonlinear dynamics.

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arXiv2025

Hands-Off Covariance Steering: Inducing Feedback Sparsity via Iteratively Reweighted $\ell_{1,p}$ Regularization

We consider the problem of optimally steering the state covariance matrix of a discrete-time linear stochastic system to a desired terminal covariance matrix, while inducing the control input to be zero over many time intervals. We propose to induce sparsity in the feedback gain matrices by using a sum-of-norms version of the iteratively reweighted $\ell_1$-norm minimization. We show that the lossless convexification property holds even with the regularization term. Numerical simulations show that the proposed method produces a Pareto front of transient cost and sparsity that is not achievable by a simple $\ell_1$-norm minimization and closely approximates the $\ell_0$-norm minimization obtained from brute-force search.

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arXiv2025

Robust Cislunar Low-Thrust Trajectory Optimization under Uncertainties via Sequential Covariance Steering

Spacecraft operations are influenced by uncertainties such as dynamics modeling, navigation, and maneuver execution errors. Although mission design has traditionally incorporated heuristic safety margins to mitigate the effect of uncertainties, particularly before/after crucial events, it is yet unclear whether this practice will scale in the cislunar region, which features locally chaotic nonlinear dynamics and involves frequent lunar flybys. This paper applies chance-constrained covariance steering and sequential convex programming to simultaneously design an optimal trajectory and trajectory correction policy that can probabilistically guarantee safety constraints under the assumed physical/navigational error models. The results show that the proposed method can effectively control the state uncertainty in a highly nonlinear environment. The framework allows faster computation and lossless convexification of linear covariance propagation compared to existing methods, enabling a rapid and accurate comparison of $ΔV_{99}$ costs for different uncertainty parameters. We demonstrate the algorithm on several transfers in the Earth-Moon Circular Restricted Three Body Problem.

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arXiv2024

Successive Convexification with Feasibility Guarantee via Augmented Lagrangian for Non-Convex Optimal Control Problems

This paper proposes a new algorithm that solves non-convex optimal control problems with a theoretical guarantee for global convergence to a feasible local solution of the original problem. The proposed algorithm extends the recently proposed successive convexification (SCvx) algorithm by addressing one of its key limitations, that is, the converged solution is not guaranteed to be feasible to the original non-convex problem. The main idea behind the proposed algorithm is to incorporate the SCvx-based iteration into an algorithmic framework based on the augmented Lagrangian method to enable the feasibility guarantee while retaining favorable properties of SCvx. Unlike the original SCvx, this approach iterates on both of the optimization variables and the Lagrange multipliers, which facilitates the feasibility guarantee as well as efficient convergence, in a spirit similar to the alternating direction method of multipliers (ADMM) for large-scale convex programming. Convergence analysis shows the proposed algorithm's strong global convergence to a feasible local optimum of the original problem and its convergence rate. These theoretical results are demonstrated via numerical examples with comparison against the original SCvx algorithm.

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arXiv2024

Robust Controller Synthesis under Markovian Mode Switching with Periodic LTV Dynamics

In this work, we propose novel LMI-based controller synthesis frameworks for periodically time-varying Markov-jump linear systems. We first discuss the necessary conditions for mean square stability and derive Lyapunov-like conditions for stability assurance. To relax strict stability requirements, we introduce a new criterion that doesn't require the Lyapunov function to decrease at each time step. Further, we incorporate these stability theorems in LMI-based controller synthesis frameworks while considering two separate problems: minimizing a quadratic cost, and maximizing the region of attraction. Numerical simulations verify the controllers' stability and showcase its applicability to fault-tolerant control.

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arXiv2024

Chance-Constrained Gaussian Mixture Steering to a Terminal Gaussian Distribution

We address the problem of finite-horizon control of a discrete-time linear system, where the initial state distribution follows a Gaussian mixture model, the terminal state must follow a specified Gaussian distribution, and the state and control inputs must obey chance constraints. We show that, throughout the time horizon, the state and control distributions are fully characterized by Gaussian mixtures. We then formulate the cost, distributional terminal constraint, and affine/2-norm chance constraints on the state and control, as convex functions of the decision variables. This is leveraged to formulate the chance-constrained path planning problem as a single convex optimization problem. A numerical example demonstrates the effectiveness of the proposed method.

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arXiv2024

Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach

This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.

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arXiv2024

Smooth Indirect Solution Method for State-constrained Optimal Control Problems with Nonlinear Control-affine Systems

This paper proposes a new indirect solution method for solving state-constrained optimal control problems by revisiting the well-established optimal control theory and addressing the long-standing issue of discontinuous control and costate due to pure state inequality constraints. It is well-known that imposing pure state path constraints in optimal control problems introduces discontinuities in the control and costate, rendering the classical indirect solution methods ineffective to numerically solve state-constrained problems. This study re-examines the necessary conditions of optimality for a class of state-constrained optimal control problems, and shows the uniqueness of the optimal control input that minimizes the Hamiltonian on constrained arcs. This analysis leads to a unifying form of optimality necessary conditions and thereby address the issue of discontinuities in control and costate by modeling them via smooth functions, transforming the originally discontinuous problems to smooth two-point boundary value problems (TPBVPs), which can be readily solved by existing nonlinear root-finding algorithms. The proposed solution method is shown to have favorable properties, including its anytime algorithm-like property, which is often a desirable property for safety-critical applications, and numerically demonstrated by an optimal orbital transfer problem.

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