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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Journal ArticleDOI
Computationally efficient NMPC for batch and semi-batch processes using parsimonious input parameterization
TL;DR: The proposed approach is illustrated in simulation on two case studies in the presence of uncertainty, namely a batch binary distillation column and a semi-batch reactor for the hydroformylation of 1-dodecene.
Journal ArticleDOI
On-Line Dynamic Data Reconciliation in Batch Suspension Polymerizations of Methyl Methacrylate
TL;DR: In this article, a phenomenological model was developed to describe the dynamic evolution of the batch suspension polymerization of methyl methacrylate in terms of reactor temperature, pressure, concentrations and molecular properties of the final polymer.
Journal ArticleDOI
2D Terminal Constrained Model Predictive Iterative Learning Control of Batch Processes With Time Delay
TL;DR: A 2D terminal constrained model predictive iterative learning control method of batch processes with time delay and Lyapunov stability theory for robust asymptotically stability of the closed-loop system is proposed.
Posted Content
Modeling metabolic networks including gene expression and uncertainties
TL;DR: This work presents a theoretical framework extending the deFBA to handle uncertainties and provide a robust solution, which is capable of handling the uncertainties in the model itself as well as uncertainties experienced by the modeled system.
Proceedings ArticleDOI
Robust nonlinear model predictive control with reduction of uncertainty via dual control
TL;DR: In this article, the authors present a dual control approach for multistage robust NMPC where the uncertainty is represented as a tree of possible realizations, and the proposed approach achieves implicit dual control actions by considering the future reduction of the ranges of the uncertainties due to control actions and measurements.
References
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Journal ArticleDOI
Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
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SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
TL;DR: An SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems is discussed and a reduced-Hessian semidefinite QP solver (SQOPT) is discussed.
Journal ArticleDOI
Robust model predictive control of constrained linear systems with bounded disturbances
TL;DR: This paper provides a novel solution to the problem of robust model predictive control of constrained, linear, discrete-time systems in the presence of bounded disturbances by solving the optimal control problem that is solved online.
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A Multiple Shooting Algorithm for Direct Solution of Optimal Control Problems
Hans Georg Bock,K.J. Plitt +1 more
TL;DR: A condensing algorithm for the solution of the approximating linearly constrained quadratic subproblems, and high rank update procedures are introduced, which are especially suited for optimal control problems and lead to significant improvements of the convergence behaviour and reductions of computing time and storage requirements.
Journal ArticleDOI
Scenarios and policy aggregation in optimization under uncertainty
TL;DR: This paper develops for the first time a rigorous algorithmic procedure for determining a robust decision policy in response to any weighting of the scenarios.