Journal ArticleDOI
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
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TLDR
In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.About:
This article is published in Journal of Process Control.The article was published on 2013-10-01. It has received 291 citations till now. The article focuses on the topics: Model predictive control & Robust control.read more
Citations
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A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie)
TL;DR: CasADi is presented, an open-source software framework for numerical optimization and algorithmic differentiation that offers a level of abstraction which is lower than algebraic modeling languages, but higher than conventional AD tools.
A General-Purpose Software Framework for Dynamic Optimization
TL;DR: CasADi as mentioned in this paper is an open-source software framework for numerical optimization and algorithmic differentiation that offers a level of abstraction which is lower than algebraic modeling languages, but higher than conventional AD tools.
Journal ArticleDOI
Constraint-Tightening and Stability in Stochastic Model Predictive Control
TL;DR: In this paper, the authors propose a constraint tightening approach to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control (SMPC), and prove asymptotic stability in probability of the minimal robust positively invariant set obtained by the unconstrained LQ-optimal controller.
Journal ArticleDOI
Handling uncertainty in economic nonlinear model predictive control: A comparative case study
TL;DR: In this paper, a multi-stage scenario-based nonlinear model predictive control (MPC) approach is proposed to deal with uncertainties in the context of economic NMPC, and a novel algorithm inspired by tube-based MPC is proposed in order to achieve a trade-off between the variability of the controlled system and the economic performance under uncertainty.
Journal ArticleDOI
Rapid development of modular and sustainable nonlinear model predictive control solutions
TL;DR: In this paper, the authors propose a modularization of the NMPC implementations that facilitates the comparison of different solutions and the transition from simulation to online application, and the proposed platform supports the multi-stage robust NMPC approach to deal with uncertainty.
References
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Book ChapterDOI
Monte Carlo Sampling Methods
TL;DR: In this article, Monte Carlo sampling methods for solving large scale stochastic programming problems are discussed, where a random sample is generated outside of an optimization procedure, and then the constructed, so-called sample average approximation (SAA), problem is solved by an appropriate deterministic algorithm.
Journal ArticleDOI
Min-max feedback model predictive control for constrained linear systems
TL;DR: The control schemes the authors discuss introduce the notion that feedback is present in the receding-horizon implementation of the control, which leads to improved performance, compared to standard model predictive control, and resolves the feasibility difficulties that arise with the min-max techniques.
Journal ArticleDOI
Brief paper: A unified framework for the numerical solution of optimal control problems using pseudospectral methods
Divya Garg,Michael A. Patterson,William W. Hager,Anil V. Rao,David Benson,Geoffrey T. Huntington +5 more
TL;DR: transformations are developed that relate the Lagrange multipliers of the discrete nonlinear programming problem to the costates of the continuous optimal control problem and the LGL costate approximation is found to have an error that oscillates about the true solution.
Journal ArticleDOI
Optimization over state feedback policies for robust control with constraints
TL;DR: It is shown that the class of admissible affine state feedback control policies with knowledge of prior states is equivalent to the classOf admissible feedback policies that are affine functions of the past disturbance sequence, which implies that a broad class of constrained finite horizon robust and optimal control problems can be solved in a computationally efficient fashion using convex optimization methods.
Journal ArticleDOI
Feedback control for optimal process operation
TL;DR: In this paper, the focus is on direct optimizing control by optimizing an economic cost criterion online over a finite horizon where the usual control specifications in terms of, e.g., product purities enter as constraints and not as set-points.