10 papers
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…
Coalition Formation with Limited Information Sharing for Local Energy Management
Luke Rickard, Paola Falugi, Eric C. Kerrigan
Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to…
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…
Exact Continuous Reformulations of Logic Constraints in Nonlinear Optimization and Optimal Control Problems
Jad Wehbeh, Eric C. Kerrigan
Many nonlinear optimal control and optimization problems involve constraints that combine continuous dynamics with discrete logic conditions. Standard approaches typically rely on…
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…