Stochastic nonlinear model predictive control of an uncertain batch polymerization reactor
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Date
2015-10
Publication Type
Conference Paper
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yes
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Abstract
This paper presents a stochastic nonlinear model predictive control technique for discrete-time uncertain nonlinear systems with particular focus on the batch polymerization reactor application. We consider a nonlinear dynamical system subject to chance constraints (i.e. need to be satisfied probabilistically up to a pre-assigned level). This formulation leads to a finite-horizon chance-constrained optimization problem at each sampling time, which is in general non-convex and hard to solve.We propose a heuristic methodology to handle uncertainty for highly nonlinear systems. In our framework, the uncertainty propagation is modelled via a Markov chain and a randomization technique, the so-called scenario approach, is employed yielding a tractable formulation. The efficiency and limitations of the proposed methodology is illustrated through its application to an uncertain batch polymerization reactor model and a comparison with deterministic nonlinear model predictive control is presented.
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Publication status
published
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Book title
5th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2015. Proceedings
Journal / series
Volume
48 (23)
Pages / Article No.
540 - 545
Publisher
Elsevier
Event
5th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2015)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Stochastic NMPC; Randomized NMPC; Uncertain batch polymerization reactor
Organisational unit
03751 - Lygeros, John / Lygeros, John