Diogo Narciso

Recent advances in multiparametric nonlinear programming

In this paper, we present recent developments in multiparametric nonlinear programming. For the case of convex problems, we highlight key issues regarding the full characterization of the parametric solution space and we discuss, through an illustrative example problem, four alternative state-of-the-art multiparametric nonlinear programming algorithms. We also identify a number of main challenges for the non-convex case and highlight future research directions.

A framework for multi-parametric programming and control — an overview

In this paper we present an overview of the recent developments in multi-parametric programming and its specific use for control. An unifying optimisation framework to solve general multi-parametric programming problems is described, with special focus given to the important class of model predictive control problems.

A combined Balanced Truncation and multi-parametric programming approach for Linear Model Predictive Control

Abstract We present a novel approach to Model Predictive Control problems, which combines a model reduction scheme coupled with parametric programming. Balanced Truncation is used to first reduce the size of the original Model Predictive Control formulation, while multi-parametric programming is employed to derive the parametric control laws offline. The theoretical developments are presented with an example problem.