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A mesh refinement method is developed for solving bang-bang optimal control problems using direct collocation. The method starts by finding a solution on a coarse mesh. Using this initial solution, the method then determines automatically…

Optimization and Control · Mathematics 2019-05-31 Yunus M. Agamawi , William W. Hager , Anil V. Rao

The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten-p norm, etc. They usually need many iterations to converge. Moreover, only the low-rank property of matrices is…

Machine Learning · Computer Science 2022-03-07 Xuelong Li , Hongyuan Zhang , Rui Zhang

A local convergence rate is established for an orthogonal collocation method based on Radau quadrature applied to an unconstrained optimal control problem. If the continuous problem has a sufficiently smooth solution and the Hamiltonian…

Numerical Analysis · Mathematics 2015-09-15 William W. Hager , Hongyan Hou , Anil V. Rao

Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However,…

Robotics · Computer Science 2020-10-19 Anirudh Vemula , Yash Oza , J. Andrew Bagnell , Maxim Likhachev

In this paper we propose a method to improve the accuracy of trajectory optimization for dynamic robots with intermittent contact by using orthogonal collocation. Until recently, most trajectory optimization methods for systems with…

Robotics · Computer Science 2019-04-03 Amir Patel , Stacey Shield , Saif Kazi , Aaron M. Johnson , Lorenz T. Biegler

Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. In times of mass-customization, low setup times including the instructing/specifying of new…

Robotics · Computer Science 2024-03-22 Jan Kristof Behrens , Ralph Lange , Masoumeh Mansouri

We develop a Magnus formalism for periodically driven systems which provides an expansion both in the driving term and the inverse driving frequency, applicable to isolated and dissipative systems. We derive explicit formulas for a driving…

Superconductivity · Physics 2016-10-14 B. Zhu , T. Rexin , L. Mathey

Prosthesis users can regain partial limb functionality, however, full natural limb mobility is rarely restored, often resulting in compensatory movements that lead to discomfort, inefficiency, and long-term physical strain. To address this…

Robotics · Computer Science 2025-08-15 Marta Lagomarsino , Robin Arbaud , Francesco Tassi , Arash Ajoudani

Rod-like metamaterials are the structures that are obtained by periodically assembling its microstructural unit (network of rods) in just one direction. In this work, we present a scheme for obtaining the nonlinear constitutive response of…

Materials Science · Physics 2026-05-13 Vinayak , Ajeet Kumar

Tip-growing eversion robots are renowned for their ability to access remote spaces through narrow passages. However, achieving reliable navigation remains a significant challenge. Existing solutions often rely on artificial muscles…

This paper is devoted to the theoretical and numerical investigation of an augmented Lagrangian method for the solution of optimization problems with geometric constraints. Specifically, we study situations where parts of the constraints…

Optimization and Control · Mathematics 2022-04-20 Xiaoxi Jia , Christian Kanzow , Patrick Mehlitz , Gerd Wachsmuth

Polynomial Chaos Expansions represent a powerful tool to simulate stochastic models of dynamical systems. Yet, deriving the expansion's coefficients for complex systems might require a significant and non-trivial manipulation of the model,…

Computation · Statistics 2012-11-13 Lorenzo Fagiano , Mustafa Khammash

Compared with traditional rigid-body robots, soft robots not only exhibit unprecedented adaptation and flexibility but also present novel challenges in their modeling and control because of their infinite degrees of freedom. Most of the…

Robotics · Computer Science 2022-09-21 Tongjia Zheng , Hai Lin

Continuum arms, such as trunk and tentacle robots, lie between the two extremities of rigid and soft robots and promise to capture the best of both worlds in terms of manipulability, dexterity, and compliance. This paper proposes a new…

Robotics · Computer Science 2019-07-16 Ali A. Nazari , Diego Castro , Isuru S. Godage

This paper addresses the autonomous robot ergodicity problem for efficient environment exploration. The spatial distribution as a reference is given by a mixture of Gaussian and the mass generation of the robot is assumed to be skinny…

Systems and Control · Electrical Eng. & Systems 2020-05-06 Rabiul Hasan Kabir , Kooktae Lee

This paper investigates online algorithms for smooth time-varying optimization problems, focusing first on methods with constant step-size, momentum, and extrapolation-length. Assuming strong convexity, precise results for the tracking…

Optimization and Control · Mathematics 2024-07-16 Liam Madden , Stephen Becker , Emiliano Dall'Anese

An isogeometric boundary element method for problems in elasticity is presented, which is based on an independent approximation for the geometry, traction and displacement field. This enables a flexible choice of refinement strategies,…

Numerical Analysis · Computer Science 2015-02-04 Benjamin Marussig , Jürgen Zechner , Gernot Beer , Thomas-Peter Fries

We propose two parameter-robust mixed finite element methods for linear Cosserat elasticity. The Cosserat coupling constant $\mu_c$, connecting the displacement $u$ and rotation vector $\omega$, leads to possible locking phenomena in finite…

Numerical Analysis · Mathematics 2025-09-19 Andrea Dziubek , Kaibo Hu , Michael Karow , Michael Neunteufel

The Max-k-Cut problem is a fundamental combinatorial optimization challenge that generalizes the classic NP-complete Max-Cut problem. While relaxation techniques are commonly employed to tackle Max-k-Cut, they often lack guarantees of…

Optimization and Control · Mathematics 2025-06-12 Yeqing Qiu , Ye Xue , Akang Wang , Yiheng Wang , Qingjiang Shi , Zhi-Quan Luo

Recently, Stochastic Gradient Descent (SGD) and its variants have become the dominant methods in the large-scale optimization of machine learning (ML) problems. A variety of strategies have been proposed for tuning the step sizes, ranging…

Machine Learning · Computer Science 2022-08-02 Xiaoyu Li