Journal ArticleDOI
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
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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
NCO-Tracking with Changing Set of Active Constraints using Multiple Solution Models
Taher Ebrahim,Reinaldo Hernández,Sankaranarayanan Subramanian,Marc Kalliski,Stefan Krämer,Sebastian Engell +5 more
TL;DR: This paper presents a scheme that extends the NCO-tracking approach to handle parametric uncertainties that change the set of active constraints and demonstrates the applicability of the methodology for the benchmark Williams-Otto semi-batch reactor.
Book ChapterDOI
NMPC based Temperature Control in Fed-batch Reactor to Avoid Thermal Runaway
TL;DR: In this paper, a Model Predictive Control (MPC) methodology is proposed to avoid reactor runaway during the optimal operation of fed-batch reactors, where the feed rate of reactant is constrained by runaway criterion (namely Modified Dynamic Condition) which increases the safety of the process operation.
Journal ArticleDOI
Stochastic Multilayer Optimization for an Acrylic Acid Reactor.
TL;DR: In this paper, a multilayer stochastic optimization approach is implemented to solve a dynamic optimization problem under uncertainties for an acrylic acid reactor, which handles different sources of uncertainties (internal, external, process).
Proceedings ArticleDOI
Adaptive Horizon Multistage Nonlinear Model Predictive Control
TL;DR: In this paper, a multistage nonlinear model predictive controller (NMPC) with a prediction horizon update using nonlinear programming (NLP) sensitivities is presented. But it does not address the non-linear programming sensitivity of the NLP.
Proceedings ArticleDOI
A comparison of control architectures for controlling a CSTR for acrylic acid production
Andres Duque,Silvia Ochoa +1 more
TL;DR: Results have shown the effectiveness of applying the Economic Nonlinear Model Predictive Controller for economic improvement and control performance of the process.
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.