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
Exploiting models of different granularity in robust predictive control
TL;DR: This work proposes a multi-stage scheme that combines the use of models of different granularity - using detailed models for short-term predictions, while performing long- term predictions with less detailed models, and shows that this scheme is recursively feasible.
Journal ArticleDOI
A Parallelizable Interior Point Method for Two-Stage Robust MPC
Emil Klintberg,Sebastien Gros +1 more
TL;DR: This paper presents a parallelizable algorithm for deploying a primal-dual interior point method on two-stage model predictive control (MPC) problems and shows that if the overhead of the parallelization is negligible, the proposed method has a computational complexity per iteration only 5%–15% higher than the state-of-the-art methods for standard MPC.
Journal ArticleDOI
2D Switched Model-Based Infinite Horizon LQ Fault-Tolerant Tracking Control for Batch Process
TL;DR: For batch processes described by 2D switched system models, the paper comes up with an infinite horizon linear quadratic fault-tolerant tracking control (LQFTTC).
Journal ArticleDOI
Robust thermal stability for batch process intensification with model predictive control
TL;DR: Worst case Model Predictive Control results in a computationally more efficient control scheme than scenario-based MPC, whilst ensuring the same extent of safety and process intensification.
Journal ArticleDOI
Optimal scheduling of flexible thermal power plants with lifetime enhancement under uncertainty
TL;DR: In this paper, the authors proposed a stochastic optimisation approach for thermal power plant scheduling with constraints on the maximum damage in critical components, including high-pressure steam drums, turbine rotors and blades, and high-temperature heat exchangers and pipes.
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.
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.