Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences

In this paper, we propose an efficient algorithm for solving a non-linear stochastic optimal control problem in discrete-time, where the true filtered solution of the original optimal control problem is obtained through solving a linear model-based optimal control problem with adjustable parameters...

Full description

Bibliographic Details
Main Authors: Kek, S., Teo, Kok Lay, Mohd Ismail, A.
Format: Journal Article
Published: 2012
Online Access:http://hdl.handle.net/20.500.11937/21845
_version_ 1848750704432250880
author Kek, S.
Teo, Kok Lay
Mohd Ismail, A.
author_facet Kek, S.
Teo, Kok Lay
Mohd Ismail, A.
author_sort Kek, S.
building Curtin Institutional Repository
collection Online Access
description In this paper, we propose an efficient algorithm for solving a non-linear stochastic optimal control problem in discrete-time, where the true filtered solution of the original optimal control problem is obtained through solving a linear model-based optimal control problem with adjustable parameters iteratively. The adjustments of these parameters are based on the differences between the real plant and the linear model that are measured. The main feature of the algorithm proposed is the integration of system optimization and parameter estimation in an interactive way so that the correct filtered solution of the original optimal control problem is obtained when the convergence is achieved. For illustration, a nonlinear continuous stirred reactor tank problem is studied. The simulation results obtained demonstrate the efficiency of the algorithm proposed.
first_indexed 2025-11-14T07:41:04Z
format Journal Article
id curtin-20.500.11937-21845
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:41:04Z
publishDate 2012
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-218452017-09-13T13:53:46Z Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences Kek, S. Teo, Kok Lay Mohd Ismail, A. In this paper, we propose an efficient algorithm for solving a non-linear stochastic optimal control problem in discrete-time, where the true filtered solution of the original optimal control problem is obtained through solving a linear model-based optimal control problem with adjustable parameters iteratively. The adjustments of these parameters are based on the differences between the real plant and the linear model that are measured. The main feature of the algorithm proposed is the integration of system optimization and parameter estimation in an interactive way so that the correct filtered solution of the original optimal control problem is obtained when the convergence is achieved. For illustration, a nonlinear continuous stirred reactor tank problem is studied. The simulation results obtained demonstrate the efficiency of the algorithm proposed. 2012 Journal Article http://hdl.handle.net/20.500.11937/21845 10.3934/naco.2012.2.207 restricted
spellingShingle Kek, S.
Teo, Kok Lay
Mohd Ismail, A.
Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
title Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
title_full Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
title_fullStr Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
title_full_unstemmed Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
title_short Filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
title_sort filtering solution of nonlinear stochastic optimal control problem in discrete-time with model-reality differences
url http://hdl.handle.net/20.500.11937/21845