Приказ основних података о дисертацији

dc.contributor.advisorAntić, Dragan
dc.contributor.otherĐorđević, Goran S.
dc.contributor.otherNikolić, Vlastimir
dc.contributor.otherMitić, Darko
dc.contributor.otherMilojković, Marko
dc.creatorPerić, Staniša LJ.
dc.date.accessioned2017-01-30T12:21:36Z
dc.date.available2017-01-30T12:21:36Z
dc.date.available2020-07-03T16:02:02Z
dc.date.issued2016-03-12
dc.identifier.urihttp://eteze.ni.ac.rs/application/showtheses?thesesId=4496
dc.identifier.urihttps://nardus.mpn.gov.rs/handle/123456789/7451
dc.identifier.urihttps://fedorani.ni.ac.rs/fedora/get/o:1225/bdef:Content/download
dc.identifier.urihttp://vbs.rs/scripts/cobiss?command=DISPLAY&base=70052&RID=533865110
dc.description.abstractThe 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.en
dc.formatapplication/pdf
dc.languagesr
dc.publisherУниверзитет у Нишу, Електронски факултетsr
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/35005/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/43007/RS//
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/262305/EU//
dc.rightsopenAccessen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceУниверзитет у Нишуsr
dc.subjectABSsr
dc.subjectABSen
dc.subjectklizni režimisr
dc.subjectortogonalni filtrisr
dc.subjectminimalna varijansasr
dc.subjectfazi regulatorsr
dc.subjectgenetički algoritamsr
dc.subjectneuronska mrežasr
dc.subjectANFISsr
dc.subjectNARXsr
dc.subjectsliding modeen
dc.subjectorthogonal filtersen
dc.subjectminimum varianceen
dc.subjectfuzzy regulatoren
dc.subjectgenetic algorithmen
dc.subjectneural networken
dc.subjectANFISen
dc.subjectNARXen
dc.titleSavremene tehnike upravljanja sistemom protiv blokiranja točkovasr
dc.typedoctoralThesisen
dc.rights.licenseBY
dcterms.abstractAнтић, Драган; Ђорђевић, Горан С.; Николић, Властимир; Митић, Дарко; Милојковић, Марко; Перић, Станиша ЛЈ.; Савремене технике управљања системом против блокирања точкова; Савремене технике управљања системом против блокирања точкова;
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/52140/Peric_Stanisa_Lj.pdf
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/52139/Disertacija7457.pdf
dc.identifier.fulltexthttps://nardus.mpn.gov.rs/bitstream/id/52139/Disertacija7457.pdf
dc.identifier.fulltexthttps://nardus.mpn.gov.rs/bitstream/id/52140/Peric_Stanisa_Lj.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_nardus_7451


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Приказ основних података о дисертацији