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
Optimization of grade transitions in polyethylene solution polymerization process under uncertainty
TL;DR: This study applies robust optimization formulations through the incorporation of back-off constraints within the optimization problem, and the resulting solution is shown to be robust under various uncertainty levels with minimal performance loss.
Journal ArticleDOI
Improving scenario decomposition algorithms for robust nonlinear model predictive control
TL;DR: This paper reviews the most common methods used for decomposition and applies them to solve robust nonlinear model predictive control problems in a distributed fashion and proposes a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge.
Journal ArticleDOI
Approximate Closed-Loop Robust Model Predictive Control With Guaranteed Stability and Constraint Satisfaction
Joel A. Paulson,Ali Mesbah +1 more
TL;DR: This work proposes a novel projection-based strategy that is capable of providing a certificate of robust feasibility and input-to-state stability in real-time and shows how this projection operator can be formulated as a parametric quadratic program that is solvable offline.
Journal ArticleDOI
Dual robust nonlinear model predictive control: A multi-stage approach
TL;DR: A dual-control approach that extends a nonlinear model predictive controller, the control actions of which are robust against the effect of model uncertainties are presented, and the results from a reactor control example show the advantage of using Dual Multi-stage NMPC over its robust adaptive counterpart.
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Nonlinear Model Predictive Control with Explicit Backoffs for Stochastic Systems under Arbitrary Uncertainty
Joel A. Paulson,Ali Mesbah +1 more
TL;DR: An extension of polynomial chaos that can handle arbitrary probability measures is proposed for a class of disturbance models representing plant-model mismatch to allow tightly guaranteeing chance constraints in NMPC for stochastic systems with arbitrary uncertainty distributions.
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.
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.