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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Dissertation
Risk-based health-aware control of Åsgard subsea gas compression station
TL;DR: In this paper, a model predictive control (MPC) approach is proposed for integrating health monitoring and control to ensure safe operation and an economic optimal control policy for the Åsgard gas compression station.
Journal ArticleDOI
Practical data-driven modeling and robust predictive control of mammalian cell fed-batch process
TL;DR: In this paper , a data-driven strategy for fast design of dynamic bioprocess models with minimal complexity is proposed, where maximum likelihood principal component analysis (MLPCA) is applied to infer the minimal reaction scheme from a 25-state mammalian cell culture database.
Journal ArticleDOI
Near-Optimal Performance of Stochastic Predictive Control
TL;DR: Under the standard stabilizability and detectability conditions, the dynamic regret of SPC is exponentially small in the prediction horizon length, and SPC can achieve near-optimal performance—the expected performance can be made arbitrarily close to the optimal solution—at a substantially reduced computational expense.
Proceedings ArticleDOI
Enabling Anticipatory Response in Multi-Stage MPC Formulation for Fully Automated Artificial Pancreas System
TL;DR: In this paper , a robust model predictive control (MPC) strategy was proposed to allow the controller to properly anticipate likely disturbances through a robust predictive control strategy, and the controller responded aggressively enough to significant glucose excursions without increasing the risk for insulin stacking.
Proceedings ArticleDOI
A Reactive Approach for Real-Time Optimization of Oil Production Under Uncertainty
TL;DR: In this article , a reactive approach based on the moving horizon estimation method for optimization in the presence of parametric uncertainty is proposed, which uses measurable outputs in order to estimate the states and uncertain parameters jointly where the full states are not measurable.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.