Search arXiv⌕ Search

arXiv · 2610.34922

Explicit Globally Asymptotically Stable Attitude Control with Reaction Wheel Angular Momentum and Torque Constraints

Abstract

We present an explicit Lyapunov attitude controller for rigid spacecraft actuated by three orthogonally mounted reaction wheels, parameterized using Modified Rodrigues Parameters (MRPs), which enforces hard bounds on wheel angular momentum and applied torque at every instant without solving any online optimization. A barrier-inspired momentum feedback prevents wheel saturation; a continuous positive scalar uniformly scales the feedback whenever a torque limit would otherwise be breached, preserving Lyapunov convergence. For the regulation problem, global asymptotic stability (GAS) is established via LaSalle's invariance principle, and constraint satisfaction is proved analytically for arbitrary symmetric positive-definite inertia matrices. Replacing the barrier by an error-state counterpart extends the design to tracking, with asymptotic (though no longer global) stability and the same safety guarantees under explicit feasibility conditions on the reference. Simulations confirm convergence and constraint satisfaction, and compare performance against other controllers.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Javier Urrios, Rafael Vazquez, Guillermo Pacheco-Ramos. 2026-10-26. Explicit Globally Asymptotically Stable Attitude Control with Reaction Wheel Angular Momentum and Torque Constraints. https://arxiv.org/abs/2610.34922

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Mini-Batch Stochastic Conjugate Gradient Algorithms with Minimal Variance

Stochastic gradient descent (SGD) methods have gained widespread popularity for solving large-scale optimization problems. However, the inherent variance in SGD often leads to slow convergence rates. We introduce a family of unbiased stochastic gradient estimators that encompasses existing estimators from the literature and identify a gradient estimator that not only maintains unbiasedness but also achieves minimal variance. Compared with the existing estimator used in SGD algorithms, the proposed estimator demonstrates a significant reduction in variance. By utilizing this stochastic gradient estimator to approximate the full gradient, we propose two mini-batch stochastic conjugate gradient algorithms with minimal variance. Under the assumptions of strong convexity and smoothness on the objective function, we prove that the two algorithms achieve linear convergence rates. Numerical experiments validate the effectiveness of the proposed gradient estimator in reducing variance and demonstrate that the two stochastic conjugate gradient algorithms exhibit accelerated convergence rates and enhanced stability.

math.OC↗

Analysis of Primal-Dual Langevin Algorithms

We analyze a recently proposed class of algorithms for the problem of sampling from probability distributions $μ^\ast$ in $\mathbb{R}^d$ with a Lebesgue density of the form $μ^\ast(x) \propto \exp(-f(Kx)-g(x))$, where $K$ is a linear operator and $f,g$ convex and non-smooth. The method is a generalization of the primal-dual hybrid gradient optimization algorithm to a sampling scheme. We give the iteration's continuous time limit, a stochastic differential equation in the joint primal-dual variable, and its mean field limit Fokker-Planck equation. Under mild conditions, the scheme converges to a unique stationary state in continuous and discrete time. Contrary to purely primal overdamped Langevin diffusion, the stationary state in continuous time does not have $μ^\ast$ as its primal marginal. Thus, further analysis is carried out to bound the bias induced by the partial dualization, and potentially correct for it in the diffusion. Time discretizations of the diffusion lead to implementable algorithms, but, as is typical in Langevin Monte Carlo methods, introduce further bias. We prove bounds for these discretization errors, which allow to give convergence results relating the produced samples to the target. We demonstrate our findings numerically first on small-scale examples in which we can exactly verify the theoretical results, and subsequently on typical examples of larger scale from Bayesian imaging inverse problems.

math.OC↗

An Efficient Parallel Solver for Linear-Equality-Constrained LQR Problems

We present a parallel solver for finite-horizon linear-quadratic regulator (LQR) problems with arbitrary stagewise linear equality constraints, using linear work and logarithmic depth in the horizon for fixed local dimensions. The solver uses a two-part reduction that separates feasibility from optimization. First, an associative suffix scan computes the states from which each remaining horizon is feasible. Second, we use these feasible-continuation sets to eliminate the constraints independently at each stage, producing an unconstrained LQR problem with smaller or equal, possibly stage-varying dimensions. A parallel unconstrained-LQR solver completes the primal solution. Finally, we recover valid multipliers from the original KKT stationarity equations using a logarithmic-depth contraction-expansion procedure, even when the multipliers are not unique. Sequential and parallel implementations are validated against dense KKT solutions and original residuals. We release a C++/CUDA implementation with JAX bindings under the MIT license.

math.OC↗