Model predictive control for nonlinear systems modelled with neutral networks

Model predictive control (MPC) is a popular and an advance control technique for linear system with hard constraints. However, in the case of non-linear system, using MPC to control may pose difficulties. This is because MPC may not be able to handle the non-linear system’s complexity. With new tech...

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Main Author: Hoong, Seng Keng
Other Authors: Soh Yeng Chai
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/71419
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-714192023-07-07T16:09:52Z Model predictive control for nonlinear systems modelled with neutral networks Hoong, Seng Keng Soh Yeng Chai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Model predictive control (MPC) is a popular and an advance control technique for linear system with hard constraints. However, in the case of non-linear system, using MPC to control may pose difficulties. This is because MPC may not be able to handle the non-linear system’s complexity. With new technologies and ideas emerging, the interest in MPC arises as it has potential to conquer this weakness and exhibits its unique strength. Neural Network (NN) is a type of classifier to predict its output and can be used as process model for many control problems. In addition, the efficient of a non-linear MPC is highly related to the properties of NN. In this research, different neural network techniques were compared through cross validation. Two layer NN using back-propagation as its learning algorithm yield the lowest errors. The number of nodes in two layer NN was evaluated through grid search and learning rate was investigated. In MPC, WMR is used as the problem description and it was observed that the MPC has a good trajectory tracking performance while handling system with nonholonomic constraints. Bachelor of Engineering 2017-05-16T08:53:54Z 2017-05-16T08:53:54Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71419 en Nanyang Technological University 82 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Hoong, Seng Keng
Model predictive control for nonlinear systems modelled with neutral networks
description Model predictive control (MPC) is a popular and an advance control technique for linear system with hard constraints. However, in the case of non-linear system, using MPC to control may pose difficulties. This is because MPC may not be able to handle the non-linear system’s complexity. With new technologies and ideas emerging, the interest in MPC arises as it has potential to conquer this weakness and exhibits its unique strength. Neural Network (NN) is a type of classifier to predict its output and can be used as process model for many control problems. In addition, the efficient of a non-linear MPC is highly related to the properties of NN. In this research, different neural network techniques were compared through cross validation. Two layer NN using back-propagation as its learning algorithm yield the lowest errors. The number of nodes in two layer NN was evaluated through grid search and learning rate was investigated. In MPC, WMR is used as the problem description and it was observed that the MPC has a good trajectory tracking performance while handling system with nonholonomic constraints.
author2 Soh Yeng Chai
author_facet Soh Yeng Chai
Hoong, Seng Keng
format Final Year Project
author Hoong, Seng Keng
author_sort Hoong, Seng Keng
title Model predictive control for nonlinear systems modelled with neutral networks
title_short Model predictive control for nonlinear systems modelled with neutral networks
title_full Model predictive control for nonlinear systems modelled with neutral networks
title_fullStr Model predictive control for nonlinear systems modelled with neutral networks
title_full_unstemmed Model predictive control for nonlinear systems modelled with neutral networks
title_sort model predictive control for nonlinear systems modelled with neutral networks
publishDate 2017
url http://hdl.handle.net/10356/71419
_version_ 1772827161095307264