5 papers · 1 filter
An Efficient Method for the Optimal Control of Microgrids Under Uncertainties using Local Reduction
Edoardo Scaccia, Eric C. Kerrigan, Anna Sadowska
The problem of optimal sizing and power scheduling in microgrids subject to uncertainties is well known to the control community. Commonly, the optimal control problem is cast as a…
Tight Bounds on Polynomials and Its Application to Dynamic Optimization Problems
Eduardo M. G. Vila, Eric C. Kerrigan, Paul Bruce
This paper presents a pseudo-spectral method for Dynamic Optimization Problems (DOPs) that allows for tight polynomial bounds to be achieved via flexible sub-intervals. The propose…
A New Duality-Free Framework for Convex Optimisation with Superlinear Convergence and Effective Warm-Starting
Michael Cummins, Eric Kerrigan
Modern second order solvers for convex optimisation, such as interior point methods, rely on primal dual information and are difficult to warm start, limiting their applicability i…
Rethinking Physics-Informed Regression Beyond Training Loops and Bespoke Architectures
Lorenzo Sabug, Eric Kerrigan
We revisit the problem of physics-informed regression, and propose a method that directly computes the state at the prediction point, simultaneously with the derivative and curvatu…
State-Dependent Uncertainty Modeling in Robust Optimal Control Problems through Generalized Semi-Infinite Programming
J. Wehbeh, E. C. Kerrigan
Generalized semi-infinite programs (generalized SIPs) are problems featuring a finite number of decision variables but an infinite number of constraints. They differ from standard…