R
Ryan Loxton
Researcher at Curtin University
Publications - 100
Citations - 2200
Ryan Loxton is an academic researcher from Curtin University. The author has contributed to research in topics: Optimal control & Optimization problem. The author has an hindex of 26, co-authored 94 publications receiving 1899 citations. Previous affiliations of Ryan Loxton include Zhejiang University.
Papers
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The control parameterization method for nonlinear optimal control: a survey
Qun Lin,Ryan Loxton,Kok Lay Teo +2 more
TL;DR: The control parameterization method is a popular numerical technique for solving optimal control problems as mentioned in this paper, which discretizes the control space by approximating the control function by a linear combination of basis functions.
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Brief paper: Optimal control problems with a continuous inequality constraint on the state and the control
TL;DR: This work considers an optimal control problem with a nonlinear continuous inequality constraint and proposes an algorithm that computes a sequence of suboptimal controls for the original problem that converges to the minimum cost.
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Brief paper: Optimal control problems with multiple characteristic time points in the objective and constraints
TL;DR: This paper develops a computational method for a class of optimal control problems where the objective and constraint functionals depend on two or more discrete time points that is approximated by a sequence of approximate optimal parameter selection problems.
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Brief paper: Optimal switching instants for a switched-capacitor DC/DC power converter
TL;DR: In this article, the authors considered a switched-capacitor DC/DC power converter with variable switching instants and formulated an equivalent optimization problem with semi-infinite constraints.
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Control parameterization for optimal control problems with continuous inequality constraints: New convergence results
TL;DR: In this article, the authors consider the control parameterization method for a class of optimal control problems in which the admissible controls are functions of bounded variation and the state and control are subject to continuous inequality constraints.