An integrated optimal control algorithm for discrete-time nonlinear stochastic system

Consider a discrete-time nonlinear system with random disturbances appearing in the real plant and the output channel where the randomly perturbed output is measurable. An iterative procedure based on the linear quadratic Gaussian optimal control model is developed for solving the optimal control of...

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Main Authors: Kek, S., Teo, Kok Lay, Ismail, A.
Format: Journal Article
Published: Taylor & Francis 2010
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/27941
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author Kek, S.
Teo, Kok Lay
Ismail, A.
author_facet Kek, S.
Teo, Kok Lay
Ismail, A.
author_sort Kek, S.
building Curtin Institutional Repository
collection Online Access
description Consider a discrete-time nonlinear system with random disturbances appearing in the real plant and the output channel where the randomly perturbed output is measurable. An iterative procedure based on the linear quadratic Gaussian optimal control model is developed for solving the optimal control of this stochastic system. The optimal state estimate provided by Kalman filtering theory and the optimal control law obtained from the linear quadratic regulator problem are then integrated into the dynamic integrated system optimisation and parameter estimation algorithm. The iterative solutions of the optimal control problem for the model obtained converge to the solution of the original optimal control problem of the discrete-time nonlinear system, despite model-reality differences, when the convergence is achieved. An illustrative example is solved using the method proposed. The results obtained show the effectiveness of the algorithm proposed.
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format Journal Article
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institution Curtin University Malaysia
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publishDate 2010
publisher Taylor & Francis
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spelling curtin-20.500.11937-279412017-09-13T16:07:20Z An integrated optimal control algorithm for discrete-time nonlinear stochastic system Kek, S. Teo, Kok Lay Ismail, A. model-reality differences optimal control iterative algorithm discrete-time nonlinear stochastic system Kalman filtering Consider a discrete-time nonlinear system with random disturbances appearing in the real plant and the output channel where the randomly perturbed output is measurable. An iterative procedure based on the linear quadratic Gaussian optimal control model is developed for solving the optimal control of this stochastic system. The optimal state estimate provided by Kalman filtering theory and the optimal control law obtained from the linear quadratic regulator problem are then integrated into the dynamic integrated system optimisation and parameter estimation algorithm. The iterative solutions of the optimal control problem for the model obtained converge to the solution of the original optimal control problem of the discrete-time nonlinear system, despite model-reality differences, when the convergence is achieved. An illustrative example is solved using the method proposed. The results obtained show the effectiveness of the algorithm proposed. 2010 Journal Article http://hdl.handle.net/20.500.11937/27941 10.1080/00207179.2010.531766 Taylor & Francis restricted
spellingShingle model-reality differences
optimal control
iterative algorithm
discrete-time nonlinear stochastic system
Kalman filtering
Kek, S.
Teo, Kok Lay
Ismail, A.
An integrated optimal control algorithm for discrete-time nonlinear stochastic system
title An integrated optimal control algorithm for discrete-time nonlinear stochastic system
title_full An integrated optimal control algorithm for discrete-time nonlinear stochastic system
title_fullStr An integrated optimal control algorithm for discrete-time nonlinear stochastic system
title_full_unstemmed An integrated optimal control algorithm for discrete-time nonlinear stochastic system
title_short An integrated optimal control algorithm for discrete-time nonlinear stochastic system
title_sort integrated optimal control algorithm for discrete-time nonlinear stochastic system
topic model-reality differences
optimal control
iterative algorithm
discrete-time nonlinear stochastic system
Kalman filtering
url http://hdl.handle.net/20.500.11937/27941