Conclusion A simple multi-parametric result of the weaning trial could help the difficult ECLS weaning decision.
Our proposed procedure features: (i) a robust reformulation/optimization step, (ii) a dynamic programming framework for the model predictive control (MPC) problem formulation, and (iii) a multi-parametric programming solution step.
A set of parametric solutions for various leaf quantities is produced by the model.
The solution of the multi-parametric programming problem provides the optimal solution as a set of parametric profiles.
The first algorithm addresses the mixed-integer case of the BLPP and employs a reformulation linearization technique (Sherali and Adams, 1990 , 1994 Adams and Sherali, 2005) and continuous multi-parametric programming for the solution of the inner problem.
This study brings out the need of simultaneous solution of multi-parametric equations (using relevant software, ORIGIN, in present case) than solution of their linearized forms, which is mostly followed by contemporary investigators.
First, an explicit/multi-parametric solution of the MHE is derived.
In the present work, we propose a novel algorithm for the analytical solution of multi-parametric MILP (mp-MILP) problems under global uncertainty, i.e. RHS, OFC and LHS.
The present paper presents multi-parametric families of quasi rational solutions of NLS of order N in terms of determinants of order 2N dependent on (2N-2) real parameters.
In this work, we present two multi-parametric programming based algorithms for the solution of integer and mixed-integer bi-level programming problems.
This paper presents an approximate multi-parametric nonlinear programming approach to explicit solution of constrained nonlinear model predictive control (MPC) problems in the presence of model uncertainty.
