Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Application of variable structure systems theory for training of intelligent systems

Basit öğe kaydını göster

dc.contributor Graduate Program in Systems and Control Engineering.
dc.contributor.advisor Kaynak, Okyay,
dc.contributor.author Yıldıran, Uğur.
dc.date.accessioned 2023-03-16T11:34:57Z
dc.date.available 2023-03-16T11:34:57Z
dc.date.issued 2001.
dc.identifier.other SCO 2001 Y55
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/15669
dc.description.abstract Soft computing architectures with their extensive flexibility and strong mapping capabilities have been widely used for control of nonlinear systems. In this regard, error backpropogation and its derivatives have been the most popular and frequently employed schemes for parameter adjustment of these architectures. However, these schemes bring some serious problems together, like instability of closed loop system and sensitivity to uncertainties, which must be carefully addressed by a system designer. In order to alleviate these problems, recently, Efe has proposed a control strategy in which parameters of intelligent controllers are updated by a continuous-time robust parameters adjustment mechanism in order to robustify and stabilize the closed loop system dynamics. The results obtained for a two link SCARA robot in this study show that the proposed method is successful in achieving the control objectives. In this thesis, the methodology proposed by Efe is investigated for first order nonlinear systems. Based on the results, it has been observed that the time evolution of input-output curves of different structures show similar characteristics. Moreover, a modification is proposed for update mechanism of all architectures in order to prevent unbounded parameter evolution problem which occurs in the original algorithm. Lastly, based on the results for different systems, it has been concluded that the Adaptive Linear Element is the most suitable architecture for the control systems investigated because of its simplicity.
dc.publisher Thesis (M.S.) - Bogazici University. Institue for Graduate Studies in Science and Engineering, 2001.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh System analysis.
dc.subject.lcsh Control theory.
dc.subject.lcsh Neural networks (Computer science)
dc.subject.lcsh Fuzzy sets.
dc.subject.lcsh Artificial Intelligence.
dc.title Application of variable structure systems theory for training of intelligent systems
dc.format.pages xvii, 101 leaves :


Bu öğenin dosyaları

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster

Dijital Arşivde Ara


Göz at

Hesabım