ADAPTIVE FUZZY SLIDING MODE CONTROL OF NONLINEAR SYSTEM

碩士 === 大同大學 === 電機工程研究所 === 89 === ABSTRACT Fuzzy logic controller is often used in the standard of fuzzy sliding mode controllers because Fuzzy Logic Systems (FLS) can be utilized to approximate complex uncertain dynamic nonlinear systems. In this thesis, we use a new simple...

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Bibliographic Details
Main Authors: Tung-Yun Kao, 高同昀
Other Authors: Chung-Chun Kung
Format: Others
Language:en_US
Published: 2001
Online Access:http://ndltd.ncl.edu.tw/handle/82785174334004024243
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Summary:碩士 === 大同大學 === 電機工程研究所 === 89 === ABSTRACT Fuzzy logic controller is often used in the standard of fuzzy sliding mode controllers because Fuzzy Logic Systems (FLS) can be utilized to approximate complex uncertain dynamic nonlinear systems. In this thesis, we use a new simple FSMC called distance-based fuzzy sliding mode controller (D-FSMC) by using a single fuzzy input variable, which is the distance from the actual state to the sliding line in the plane. Hence, the number of fuzzy rules is greatly reduced compared to the case of the conventional FSMC with two fuzzy inputs. The stability of the fuzzy control system is guaranteed using the Lyapunov stability. In addition, an adaptive fuzzy sliding mode control (SMC) scheme that FLS is used to approximate the unknown systems is proposed. In order to reduce the approximation errors between the true nonlinear function and FLS, an adaptive law is presented. Moreover, we provide a weighting factor. It can adjust the plant and control information by weighting factor, that is, if the plant information is more important and reliable than control information, we should choose a larger factor; otherwise, a smaller factor should be chosen. At last, simulation results show that the proposed controller has the following advantages: 1. It requires only one input variable, signed distance, regardless of the complexity of the controlled plants. 2. The control rule table is constructed on 1-D space. 3. The proposed controller can well control most of the complex systems without known their mathematical model. 4. Less information about the plant and control knowledge are require (if you only known one of the human knowledge). Because the adaptation law can help to learn the dynamics during real-time operation.