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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Proceedings ArticleDOI
Identifying Attacks on Nonlinear Cyber-Physical Systems in a Robust Model Predictive Control Setup.
TL;DR: This paper presents a setup consisting of two interlacing approaches towards secure control of multi-agent nonlinear dynamic systems under attack, and proposes an attack identification method based on ideas from signal recovery.
Proceedings ArticleDOI
Robust Tube-Enhanced Multi-Stage NMPC With Stability Guarantees
TL;DR: In this paper , the authors proposed a robust nonlinear model predictive control (NMPC) scheme that provides an improved trade-off between optimality and complexity when compared to other available strategies.
Journal ArticleDOI
Dynamic real-time optimization of batch processes using Pontryagin’s minimum principle and set-membership adaptation
Radoslav Paulen,Miroslav Fikar +1 more
TL;DR: This study studies a dynamic real-time optimization in the context of model-based time-optimal operation of batch processes under parametric model mismatch and proposes an approach that uses parameterized conditions of optimality in the adaptive predictive-control fashion.
Journal ArticleDOI
A General Framework for Learning-Based Distributionally Robust MPC of Markov Jump Systems
TL;DR: In this article , a data-driven learning model predictive control (MPC) scheme for chance-constrained Markov jump systems with unknown switching probabilities is presented, and the authors prove recursive feasibility of the resulting scheme and show that the original chance constraints remain satisfied at every time step.
Journal ArticleDOI
Improved Robust Predictive Control for Lur’e Systems Using Set-based Learning
TL;DR: A robust model predictive control scheme for Lur’e systems subject to constraints is presented, which improves via learning over time and allows efficient implementation using Linear Matrix Inequalities.
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.