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
Optimal polymer grade transitions for fluidized bed reactors
TL;DR: In this article, the gas-phase catalytic polymerization is modeled in a fluidized bed reactor by a single-phase model, and dynamic optimization is implemented to determine optimal operating sequences for grade changes.
Journal ArticleDOI
Economic stochastic nonlinear model predictive control of a semi-batch polymerization reaction
TL;DR: This paper proposes a framework for output feedback stochastic nonlinear model predictive control (SNMPC) to consider the uncertainties explicitly, which are assumed to follow known probability distributions.
Journal ArticleDOI
Robust Optimizing Control of Fermentation Processes Based on a Set of Structurally Different Process Models
TL;DR: It is shown in simulation studies of a CHO cultivation process that the usage of multiple, adapted models as scenarios improves the accuracy of the state estimation and the overall process performance.
Proceedings ArticleDOI
A non-conservative robust output feedback MPC for constrained linear systems
TL;DR: This work proposes and demonstrates a non-conservative output feedback scheme within the multi-stage MPC framework for linear time-invariant systems and proposes a new approach to solving plant-model mismatch and disturbances.
Journal ArticleDOI
Parallel Solution of Robust Nonlinear Model Predictive Control Problems in Batch Crystallization
TL;DR: This paper solves the optimization problems resulting from a robust nonlinear model predictive control strategy at each sampling instance using the parallel Schur complement method developed to solve stochastic programs on distributed and shared memory machines.
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.