Zobrazeno 1 - 10
of 279
pro vyhledávání: '"Kerrigan, Eric C."'
Autor:
Nita, Lucian, Kerrigan, Eric C.
We propose joining a flexible mesh design with an integrated residual transcription in order to improve the accuracy of numerical solutions to optimal control problems. This approach is particularly useful when state or input trajectories are non-smo
Externí odkaz:
http://arxiv.org/abs/2410.23037
Autor:
Wehbeh, Jad, Kerrigan, Eric C.
We examine robust output feedback control of discrete-time nonlinear systems with bounded uncertainties affecting the dynamics and measurements. Specifically, we demonstrate how to construct semi-infinite programs that produce gains to minimize some
Externí odkaz:
http://arxiv.org/abs/2409.08684
Autor:
Wehbeh, Jad, Kerrigan, Eric C.
Discrete-time robust optimal control problems generally take a min-max structure over continuous variable spaces, which can be difficult to solve in practice. In this paper, we extend the class of such problems that can be solved through a previously
Externí odkaz:
http://arxiv.org/abs/2404.05635
Publikováno v:
IFAC-PapersOnLine Volume 56, Issue 2, 2023, Pages 10576-10581
We propose a novel early-terminating mesh refinement strategy using an integrated residual method to solve dynamic feasibility problems. As a generalization of direct collocation, the integrated residual method is used to approximate an infinite-dime
Externí odkaz:
http://arxiv.org/abs/2403.07811
Autor:
Vila, Eduardo M. G., Kerrigan, Eric C.
Polynomials are widely used to represent the trajectories of states and/or inputs. It has been shown that a polynomial can be bounded by its coefficients, when expressed in the Bernstein basis. However, in general, the bounds provided by the Bernstei
Externí odkaz:
http://arxiv.org/abs/2403.07707
In contrast to set-point tracking which aims to reduce the tracking error between the tracker and the reference, tracking-in-range problems only focus on whether the tracker is within a given range around the reference, making it more suitable for th
Externí odkaz:
http://arxiv.org/abs/2403.03066
Active flow control for drag reduction with reinforcement learning (RL) is performed in the wake of a 2D square bluff body at laminar regimes with vortex shedding. Controllers parameterised by neural networks are trained to drive two blowing and suct
Externí odkaz:
http://arxiv.org/abs/2307.12650
Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the contr
Externí odkaz:
http://arxiv.org/abs/2303.08540
Publikováno v:
Proc. 61st IEEE Conference on Decision and Control, 2022
Solutions to optimal control problems can be discontinuous, even if all the functionals defining the problem are smooth. This can cause difficulties when numerically computing solutions to these problems. While conventional numerical methods assume s
Externí odkaz:
http://arxiv.org/abs/2211.06279
Autor:
Nita, Lucian, Vila, Eduardo M. G., Zagorowska, Marta A., Kerrigan, Eric C., Nie, Yuanbo, McInerney, Ian, Falugi, Paola
Publikováno v:
Proc. 20th European Control Conference (ECC 2022)
Introducing flexibility in the time-discretisation mesh can improve convergence and computational time when solving differential equations numerically, particularly when the solutions are discontinuous, as commonly found in control problems with cons
Externí odkaz:
http://arxiv.org/abs/2205.08613