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
On the Practical Design of Tube-Enhanced Multi-Stage Nonlinear Model Predictive Control
TL;DR: In this article , a tube-enhanced multi-stage nonlinear model predictive control is proposed to handle a wide range of uncertainties with reduced conservatism and manageable computational complexity, and the authors elaborate on the flexibility of the approach from an application point of view.
Proceedings ArticleDOI
Explainable artificial intelligence for deep learning-based model predictive controllers
TL;DR: In this paper , an explainable artifical intelligence technique is proposed to generate insights from learning-based model predictive control for the purpose of model debugging and simplification, which can also represent a better alternative to dimensionality reduction techniques such as principal component analysis.
Journal ArticleDOI
Robust design of optimal experiments considering consecutive re-designs
TL;DR: In this article , the authors investigate the problem of robust design of experiments (rDoE) in the context of nonlinear maximum-likelihood parameter estimation, where an experimenter designs a series of experiments with the possibility of a re-design after a particular experiment run.
Proceedings ArticleDOI
Explainable artificial intelligence for deep learning-based model predictive controllers
TL;DR: In this paper , an explainable artifical intelligence technique is used to generate insights from learning-based MPC for the purpose of model debugging and simplification, which can also represent a better alternative to dimensionality reduction techniques such as principal component analysis.
Posted Content
Online Gaussian Process learning-based Model Predictive Control with Stability Guarantees.
TL;DR: This work presents a combination of a output feedback model predictive control scheme, which does not require full state information, and a Gaussian process prediction model that is capable of online learning and guarantees input-to-state stability w.r.t. to the model-plant mismatch.
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.
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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.