This overview paper reviews numerical methods for solution of optimal control problems in
real-time, as they arise in nonlinear model predictive control (NMPC) as well as in moving …

This paper is about distributed derivative-based algorithms for solving optimization problems
with a separable (potentially nonconvex) objective function and coupled affine constraints. A …

1.1 Introduction Feedback control based on an online optimization of nonlinear dynamic
process models subject to constraints, and its special case, nonlinear model predictive …

Our quality of life, the world's productivity and its sustainability become more and more
determined by the outcome and benefits of process automation. In this domain of automatic …

This paper proposes an algorithmic framework for solving parametric optimization problems
which we call adjoint-based predictor-corrector sequential convex programming. After …

Abstract Model predictive control (MPC) has been effectively applied in process industries
since the 1990s. Models in the form of closed equation sets are normally needed for MPC …

Direct optimal control methods first discretize a continuous-time Optimal Control Problem
(OCP) and then solve the resulting Nonlinear Program (NLP). Sequential Quadratic …

This paper presents a class of efficient Newton-type algorithms for solving the nonlinear
programs (NLPs) arising from applying a direct collocation approach to continuous time …

In recent years, efficient optimization algorithms for nonlinear model predictive control
(NMPC) have been proposed, that significantly reduce the online computational time. In …

Background Model development is a key task in systems biology, which typically starts from
an initial model candidate and, involving an iterative cycle of hypotheses-driven model …