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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Robust Tube-enhanced Multi-stage Output Feedback MPC for Linear Systems with Additive and Parametric Uncertainties
TL;DR: A new robust output feedback Model Predictive Control (MPC) scheme that can handle both additive and parametric uncertainties using the tube-enhanced multi-stage (TEMS) MPC framework is proposed.
Posted Content
Particle MPC for Uncertain and Learning-Based Control.
TL;DR: In this paper, a nonlinear particle model predictive control (PMPC) approach is proposed to control under uncertainty, which directly incorporates any particle-based uncertainty representation, such as those common in robotics.
A Review of Multistage Research Studies Examining Social Network Dynamics Involving Dynamic Network Analysis and Response Surface Methodology: A SPIDER Approach
TL;DR: In this paper, the SPIDER tool (Sample, Phenomenon of Interest, Design, Evaluation, Research Type) was used to develop a search protocol to conduct a systematic review on studies examining social network dynamics to inform optimal leadership characteristics such as effectiveness and performance.
Proceedings ArticleDOI
Towards Personalized Plasma Medicine via Data-Efficient Adaptation of Fast Deep Learning-based MPC Policies
TL;DR: In this paper , a data-efficient, globally optimal strategy was proposed to adapt deep learning-based controllers that can be readily embedded on resource-limited hardware for portable medical devices, and the proposed strategy employs multi-objective Bayesian optimization to adapt parameters of a deep neural network-based control law using observations of closed-loop performance measures.
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.
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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.