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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Proceedings ArticleDOI
Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees
TL;DR: In this paper, a learning and scenario-based MPC (L-sMPC) strategy is proposed that systematically accounts for feedback in the prediction using a state and input-dependent scenario tree computed from a Gaussian process uncertainty model.
Journal ArticleDOI
Economics optimizing control of a multi-product reactive distillation process under model uncertainty
Daniel Haßkerl,Clemens Lindscheid,Sankaranarayanan Subramanian,Patrick Diewald,Alexandru Tatulea-Codrean,Sebastian Engell +5 more
TL;DR: This paper discusses economics optimizing control of a very complex process, a multi-product transesterification reaction that is realized in a reactive distillation process using simulation studies.
Journal ArticleDOI
Multi-stage economic model predictive control for a gold cyanidation leaching process under uncertainty
Runda Jia,Runda Jia,Fengqi You +2 more
Journal ArticleDOI
Predicting industrial polymer melt index via incorporating chaotic characters into Chou's general PseAAC
TL;DR: A novel MI prediction model is proposed based on least squares support vectors (LSSVM) and chaos theory, and the particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of LSSVM, and an optimal prediction model, PSO–Chaos–L SSVM, is thereby developed.
Posted Content
Heterogeneously parameterized tube model predictive control for LPV systems
TL;DR: A specific parameterization that combines the principles of scenario and homothetic tube MPC is proposed and it is shown to satisfy the required conditions and its capability of achieving improved complexity/performance trade-offs is demonstrated using two numerical examples.
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.