Title
Savremene tehnike upravljanja sistemom protiv blokiranja točkova
Creator
Perić, Staniša LJ. 1985-
Copyright date
2015
Object Links
Select license
Autorstvo 3.0 Srbija (CC BY 3.0)
License description
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 12.03.2016.
Other responsibilities
mentor
Antić, Dragan
član komisije
Đorđević, Goran S.
član komisije
Nikolić, Vlastimir
član komisije
Mitić, Darko
član komisije
Milojković, Marko 1980-
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Nišu
Faculty
Elektronski fakultet
Group
Katedra za automatiku
Alternative title
Advanced control techniques in anti-lock braking system
Publisher
[S. LJ. Perić]
Format
[11], 165 str.
description
Biobibliografski podaci: str. 142-165
description
Automatics
Abstract (en)
The main goal of research in this PhD dissertation is to investigate the possibilities of
application of modern control methods in anti-lock braking system (ABS), in order to
increase the safety of passengers in traffic during vehicle emergency braking. The complete
historical overview of ABS development is also presented, as well as the basic components of
the system. Bearing in mind that the testing of newly designed algorithms is impractical on
the real system, the laboratory experimental setup of ABS is used. The modeling of system
using different methods is performed first, resulting in several models, where each of them
could be used during the design of a specific control method. Since it is demonstrated that the
model describing the dynamics of ABS is quite nonlinear, a special emphasis is placed on the
use of sliding mode control, both in the continuous- and discrete-time domains.
This dissertation also analyzes the possibility of combining sliding mode control with
different intelligent control methods, such as fuzzy control systems, genetic algorithms and
neural networks, all with the aim of overcoming the shortcomings of the certain control
methods and improving system performances. Fuzzy control theory and genetic algorithms
are implemented in setting the parameters of control laws, eliminating the need to adjust the
parameters by trial and error method. In the domain of neural networks, the significant
modifications in the traditional adaptive neuro-fuzzy inference system (ANFIS) are
introduced, whereby almost orthogonal functions are inserted in particular network layer. The
further network adaptation is performed by introducing external stimulus in the form of
hormone secretion from the glands of the endocrine system. It is also designed a new
structure consisting of almost orthogonal endocrine neural networks and nonlinear
autoregressive neural network with external input (NARX) that is used during the prediction
of modeling error.
In the end, it is important to emphasize that the justification for introducing and the
effectiveness of the proposed control algorithms are verified by a series of laboratory
experiments with a comparative analysis of the obtained results with the results of the
application of well-known control methods.
Authors Key words
ABS, klizni režimi, ortogonalni filtri, minimalna varijansa, fazi regulator,
genetički algoritam, neuronska mreža, ANFIS, NARX
Authors Key words
ABS, sliding mode, orthogonal filters, minimum variance, fuzzy regulator,
genetic algorithm, neural network, ANFIS, NARX
Classification
(681.518.52+681.5.01):62-529.3/.5
Subject
681.518.52+681.5.01]:629-592.3/.5(043.3)
Subject
T 125
Type
Elektronska teza
Abstract (en)
The main goal of research in this PhD dissertation is to investigate the possibilities of
application of modern control methods in anti-lock braking system (ABS), in order to
increase the safety of passengers in traffic during vehicle emergency braking. The complete
historical overview of ABS development is also presented, as well as the basic components of
the system. Bearing in mind that the testing of newly designed algorithms is impractical on
the real system, the laboratory experimental setup of ABS is used. The modeling of system
using different methods is performed first, resulting in several models, where each of them
could be used during the design of a specific control method. Since it is demonstrated that the
model describing the dynamics of ABS is quite nonlinear, a special emphasis is placed on the
use of sliding mode control, both in the continuous- and discrete-time domains.
This dissertation also analyzes the possibility of combining sliding mode control with
different intelligent control methods, such as fuzzy control systems, genetic algorithms and
neural networks, all with the aim of overcoming the shortcomings of the certain control
methods and improving system performances. Fuzzy control theory and genetic algorithms
are implemented in setting the parameters of control laws, eliminating the need to adjust the
parameters by trial and error method. In the domain of neural networks, the significant
modifications in the traditional adaptive neuro-fuzzy inference system (ANFIS) are
introduced, whereby almost orthogonal functions are inserted in particular network layer. The
further network adaptation is performed by introducing external stimulus in the form of
hormone secretion from the glands of the endocrine system. It is also designed a new
structure consisting of almost orthogonal endocrine neural networks and nonlinear
autoregressive neural network with external input (NARX) that is used during the prediction
of modeling error.
In the end, it is important to emphasize that the justification for introducing and the
effectiveness of the proposed control algorithms are verified by a series of laboratory
experiments with a comparative analysis of the obtained results with the results of the
application of well-known control methods.
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