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
Robust distributed model predictive control for systems of parallel structure within process networks
TL;DR: A novel robust distributed model predictive control algorithm is developed for such parallel systems which deals explicitly with competitive couplings, competitive constraints and uncertainties.
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Globally optimal nonlinear model predictive control based on multi-parametric disaggregation
TL;DR: In this article, the continuous process model is transformed into a nonlinear programming (NLP) problem via discretization which uses an implicit integration method, and the NLP problem is relaxed into a mixed integer linear programming (MILP) model.
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A Synergistic Approach to Robust Output Feedback Control: Tube-based Multi-stage NMPC
TL;DR: A robust output feedback NMPC scheme that is real-time implementable and provides robust constraint satisfaction in the presence of parametric and additive uncertainties, and estimation errors is formulated.
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Architectures for Neural Networks as Surrogates for Dynamic Systems in Chemical Engineering
TL;DR: In this article, a dynamic surrogate model using LSTM layers for a batch distillation system is presented, which is valid from start-up until shutdown, and hyperparameter tuning by Bayesian and Bandit optimization is included.
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Architectures for neural networks as surrogates for dynamic systems in chemical engineering
TL;DR: In this paper , a dynamic surrogate model using LSTM layers for a batch distillation system is presented, which is valid from start-up until shutdown, and hyperparameter tuning by Bayesian and Bandit optimization is included.
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.
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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.