1 citations · 2 across the 15 of their papers we have counts for
16 papers
Direct Inference on Admissible Sets (Diona)
Lorenzo Sabug, Eric Kerrigan
We propose a new regression method named Direct Inference on Admissible Sets (Diona) that directly uses samples to estimate the state and its derivatives at a query point, along wi…
Generalized Semi-Infinite Programming for Robust Optimal Control with Decision-Dependent Uncertainty
J. Wehbeh, E. C. Kerrigan, E. Scaccia
Generalized semi-infinite programs (GSIPs) arise in robust optimal control whenever the admissible uncertainty depends on the state or controls. Existing GSIP methods either impose…
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…
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…
Optimistic vs Pessimistic Uncertainty Model Unfalsification
Jannes Hühnerbein, Jad Wehbeh, Eric C. Kerrigan
We present a novel, input-output data-driven approach to uncertainty model identification. As the true bounds and distributions of system uncertainties ultimately remain unknown, w…
Smooth Logic Constraints in Nonlinear Optimization and Optimal Control Problems
J. Wehbeh, E. C. Kerrigan
In some optimal control problems, complex relationships between states and inputs cannot be easily represented using continuous constraints, necessitating the use of discrete logic…
Update-Aware Robust Optimal Model Predictive Control for Nonlinear Systems
J. Wehbeh, E. C. Kerrigan
Robust optimal or min-max model predictive control (MPC) approaches aim to guarantee constraint satisfaction over a known, bounded uncertainty set while minimizing a worst-case per…
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…
Accurate Solutions to Optimal Control Problems via a Flexible Mesh and Integrated Residual Transcription
Lucian Nita, Eric C. Kerrigan
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 approa…
Robust Output Feedback of Nonlinear Systems through the Efficient Solution of Min-Max Optimization Problems
Jad Wehbeh, Eric C. Kerrigan
We examine robust output feedback control of discrete-time nonlinear systems with bounded uncertainties affecting the dynamics and measurements. Specifically, we demonstrate how to…
Semi-Infinite Programs for Robust Control and Optimization: Efficient Solutions and Extensions to Existence Constraints
Jad Wehbeh, Eric C. Kerrigan
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 ext…
Mesh Refinement with Early Termination for Dynamic Feasibility Problems
Eduardo M. G. Vila, Eric C. Kerrigan, Paul Bruce
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,…
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…