Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method

In this paper, a hybrid pseudo-spectral (hPS) method is utilized to analyze the uncertainty of the model predictive control (MPC) for nonlinear systems with stochastic parameter uncertainty. The hPS method incorporates Generalized polynomial expansion (gPC) and pseudo-spectral (PS) optimal control i...

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Autores principales:	Ali Namadchian, Mehdi Ramezani
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	model predictive control uncertainty analysis generalized polynomial chaos pseudo-spectral optimal control Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7567000104154543b04930f1a3481dc5
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spelling	oai:doaj.org-article:7567000104154543b04930f1a3481dc52021-11-04T15:51:56ZUncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method2331-191610.1080/23311916.2019.1691803https://doaj.org/article/7567000104154543b04930f1a3481dc52019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1691803https://doaj.org/toc/2331-1916In this paper, a hybrid pseudo-spectral (hPS) method is utilized to analyze the uncertainty of the model predictive control (MPC) for nonlinear systems with stochastic parameter uncertainty. The hPS method incorporates Generalized polynomial expansion (gPC) and pseudo-spectral (PS) optimal control in a hybrid format. To quantify the effect of uncertainty on the MPC states and control inputs, we use the gPC method, and for time discretization of the MPC problem, the pseudo-spectral method is employed. The proposed method will be compared with the well-known Monte Carlo (MC) simulation method in terms of computational speed. Two dynamical systems are tested. First, an industrial process, continuous stirred reactor tank, which is a highly nonlinear system, with Gaussian distributed parameter uncertainty and second, a typical second-order dynamical system with uniform and Weibull distributed parameter uncertainty. Simulation results demonstrate that the proposed method can approximate the two first moments of the states and control inputs under MPC much faster and computationally more efficient than MC simulations.Ali NamadchianMehdi RamezaniTaylor & Francis Grouparticlemodel predictive controluncertainty analysisgeneralized polynomial chaospseudo-spectral optimal controlEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	model predictive control uncertainty analysis generalized polynomial chaos pseudo-spectral optimal control Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	model predictive control uncertainty analysis generalized polynomial chaos pseudo-spectral optimal control Engineering (General). Civil engineering (General) TA1-2040 Ali Namadchian Mehdi Ramezani Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
description	In this paper, a hybrid pseudo-spectral (hPS) method is utilized to analyze the uncertainty of the model predictive control (MPC) for nonlinear systems with stochastic parameter uncertainty. The hPS method incorporates Generalized polynomial expansion (gPC) and pseudo-spectral (PS) optimal control in a hybrid format. To quantify the effect of uncertainty on the MPC states and control inputs, we use the gPC method, and for time discretization of the MPC problem, the pseudo-spectral method is employed. The proposed method will be compared with the well-known Monte Carlo (MC) simulation method in terms of computational speed. Two dynamical systems are tested. First, an industrial process, continuous stirred reactor tank, which is a highly nonlinear system, with Gaussian distributed parameter uncertainty and second, a typical second-order dynamical system with uniform and Weibull distributed parameter uncertainty. Simulation results demonstrate that the proposed method can approximate the two first moments of the states and control inputs under MPC much faster and computationally more efficient than MC simulations.
format	article
author	Ali Namadchian Mehdi Ramezani
author_facet	Ali Namadchian Mehdi Ramezani
author_sort	Ali Namadchian
title	Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
title_short	Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
title_full	Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
title_fullStr	Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
title_full_unstemmed	Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
title_sort	uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/7567000104154543b04930f1a3481dc5
work_keys_str_mv	AT alinamadchian uncertaintyquantificationofmodelpredictivecontrolfornonlinearsystemswithparametricuncertaintyusinghybridpseudospectralmethod AT mehdiramezani uncertaintyquantificationofmodelpredictivecontrolfornonlinearsystemswithparametricuncertaintyusinghybridpseudospectralmethod
_version_	1718444657518575616

Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method

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