Journal ArticleDOI
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Learning-based approximation of robust nonlinear predictive control with state estimation applied to a towing kite
Benjamin Karg,Sergio Lucia +1 more
TL;DR: This work uses a complex robust NMPC approach to generate data pairs that are used to learn an approximate robust controller which is robust to model uncertainties and proposes a statistical verification strategy to compute backoffs that lead to the satisfaction of important constraints despite the presence of estimation, measurement and approximation errors.
Proceedings ArticleDOI
Contingency Model Predictive Control for Automated Vehicles
TL;DR: The Contingency Model Predictive Control (CMPC) as mentioned in this paper is a novel and implementable control framework which tracks a desired path while simultaneously maintaining a contingency plan -an alternate trajectory to avert an identified potential emergency.
Journal ArticleDOI
Efficient Robust Economic Nonlinear Model Predictive Control of an Industrial Batch Reactor
TL;DR: In this article, a real-time decision problem under uncertainty is formulated as a multi-stage stochastic problem with recourse, based on a description of the uncertainty by a scenario tree.
Journal ArticleDOI
Toward a Unifying Framework Blending Real-Time Optimization and Economic Model Predictive Control
TL;DR: This paper proposes a conceptual framework to blend ideas from (output) modifier adaptation and offset-free economic MPC with recent results on economicMPC without terminal constraints and alleviates the need for a dedicated computation of steady-state targets by exploiting the turnpike property in the open-loop predictions.
Journal ArticleDOI
Optimal control in chemical engineering: Past, present and future
Eduardo Nolasco,Vassilios S. Vassiliadis,Walter Kähm,Sai Darshan Adloor,Riham Al Ismaili,Raúl Conejeros,Thomas Espaas,Nishanthi Gangadharan,Vasileios Mappas,Felipe Scott,Qianyue Zhang +10 more
TL;DR: A brief overview of the theory of optimal control is offered, spanning from its roots in calculus of variations to Pontryagin’s maximum principle and some of its extensions, as well as selected applications currently found in literature–ranging from classical chemical engineering systems to bioprocesses.
References
More filters
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.