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
An improved dual Newton strategy for scenario-tree MPC
TL;DR: It is shown that it is possible to organize the robust MPC problem such that the dual Hessian has a block-tridiagonal structure, hence reducing dramatically the cost of its factorization, and a simple and inexpensive strategy of constraint elimination is proposed for ensuring the positive definiteness of theDual Hessian, making regularization superfluous.
Anticipative model predictive control for linear parameter-varying systems
TL;DR: The final author version and the galley proof are versions of the publication after peer review and the final published version features the final layout of the paper including the volume, issue and page numbers.
Journal ArticleDOI
Output feedback stochastic nonlinear model predictive control for batch processes
Eric Bradford,Lars Imsland +1 more
TL;DR: A shrinking horizon NMPC algorithm accounting for uncertainties to optimize a probabilistic objective subject to chance constraints and considers feedback by using time-invariant linear feedback gains, which alleviates the conservativeness of the approach.
Proceedings ArticleDOI
Efficient stochastic model predictive control based on polynomial chaos expansions for embedded applications
TL;DR: This work uses polynomial chaos theory to propagate the uncertainty through the dynamics of a linear system in order to obtain explicit expressions of the mean and variance of the future states that can be used to formulate an stochastic MPC problem with chance constraints.
Dissertation
Model predictive control for autonomous and cooperative driving
TL;DR: In this article, a hierarchical control architecture for individual vehicle control that decomposes the controller into a motion planner and a tracking controller using nonlinear MPC is presented. And a hierarchical architecture composed of an intersection controller and multiple local vehicle controllers is proposed to allow vehicles to cross the intersection smoothly and safely.
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.