A study on neuro sliding mode control for nonminimum phase systems having an unstable internal dynamics불안정한 내부 동역학을 가진 비최소 위상 시스템에 대한 뉴로 슬라이딩 모드 제어에 관한 연구

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dc.contributor.advisorPark, Cheol-Hoon-
dc.contributor.advisor박철훈-
dc.contributor.authorLee, Ha-Joon-
dc.contributor.author이하준-
dc.date.accessioned2011-12-14-
dc.date.available2011-12-14-
dc.date.issued2007-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=268732&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/35426-
dc.description학위논문(박사) - 한국과학기술원 : 전기및전자공학전공, 2007. 8, [ ix, 113 p. ]-
dc.description.abstractIn this dissertation, new sliding mode control methods using neural networks are developed. Although the conventional sliding mode control is simple and insensitive to uncertainties, it has three main problems. The first one is chattering due to the discontinuous switching function in the control input. The second one is the assumption of known bounds of uncertainties. The last one is how to control the magnitude of undershoot in nonminimum phase (NMP) systems. In order to overcome these problems, we propose a new neuro sliding control using neural networks as a feedback controller. Multilayer neural networks with an error back-propagation learning algorithm are used to compensate for arbitrary uncertainties existing in the system. By virtue of the action of neural networks, the proposed control scheme reduces the steady state error which is intrinsically occurred by the boundary layer method for alleviating the chattering. Moreover, the proposed controller does not require a priori knowledge of the bounds of uncertainties. We show that the performance of tracking control depends on the learning capability of neural networks of which the learning rates can be obtained from the local convergence condition of the weight update rule. In order to control the magnitude of undershoot in NMP systems, a new control scheme using two sliding manifolds is proposed. The control design is based on two sliding manifolds which are in charge of control in turn. One is used for convergence of the state trajectory to the origin and the other for maintaining the magnitude of undershoot to the desired value. The stability of the proposed control scheme is proved with Lyapunov function method. Computer simulations show the effectiveness of the proposed control scheme.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectnonminimum phase system-
dc.subjectundershoot-
dc.subjectneuro control-
dc.subjectneural network-
dc.subjectsliding mode control-
dc.subject비최소 위상 시스템-
dc.subject하향초과-
dc.subject뉴로 제어-
dc.subject신경 회로망-
dc.subject슬라이딩 모드 제어-
dc.subjectnonminimum phase system-
dc.subjectundershoot-
dc.subjectneuro control-
dc.subjectneural network-
dc.subjectsliding mode control-
dc.subject비최소 위상 시스템-
dc.subject하향초과-
dc.subject뉴로 제어-
dc.subject신경 회로망-
dc.subject슬라이딩 모드 제어-
dc.titleA study on neuro sliding mode control for nonminimum phase systems having an unstable internal dynamics-
dc.title.alternative불안정한 내부 동역학을 가진 비최소 위상 시스템에 대한 뉴로 슬라이딩 모드 제어에 관한 연구-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN268732/325007 -
dc.description.department한국과학기술원 : 전기및전자공학전공, -
dc.identifier.uid020025243-
dc.contributor.localauthorPark, Cheol-Hoon-
dc.contributor.localauthor박철훈-
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