Discrete-Time Adaptive Fuzzy Logic Control of Feedback Linearizable Systems
In this article we utilize the use of fuzzy systems as function approximators for the design of an adaptive fuzzy controller (FC). The approximation properties of fuzzy systems per se have been extensively studied in [Buckley, 92; Kosko, 92; Kosko, 94; Langari and Tomizuka, 91; Wang and Mendel, 92; Wang, 94, Ying, 93; Ying, 94; Zeng and Singh, 95]. The results reported in [Wang and Mendel, 92; Zeng and Singh, 95] show that fuzzy associate memory functions (FAM) are universal approximators for certain classes of functions.
KeywordsFuzzy System Tracking Error Fuzzy Controller Auxiliary Input Adaptation Gain
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- Jagannathan, S., Lewis, F. L., Vandegrift, M., and Commuri, S. (1996c) “Adaptive fuzzy logic control of feedback linearizable nonlinear systems,” Proceedings of the ISAI/IFIS, Cancun, Mexico, pp.385–392.Google Scholar
- Kosko, B. (1994),Fuzzy systems as universal approximators IEEE Trans. on Computers, vol. 43, no. 10.Google Scholar
- Langari, R. and Tomizuka, M. (1991), “Analysis and stability of a class of fuzzy linguistic controllers with internal dynamics,” Proceedings ASME Winter Annual Meeting, paper 91-WA-DSC-13.Google Scholar
- Lewis, F. L. and Liu, K. (1995), “Towards a paradigm for fuzzy logic control,” Automatica. Google Scholar
- Polycarpou, M. M. and Ioannou, P. A. (1991), “Identification and control using neural network models: design and stability analysis,” Dept. of Elec. Engg., Tech Report.91–09–01.Google Scholar
- Vandegrift, M., Lewis, F. L., Jagannathan, S., and Liu, K. (1995), “Fuzzy logic control of a class of discrete-time nonlinear systems”, Proc. of the IEEE Symposium on Intelligent Control, pp.395–401.Google Scholar
- Wang, L-X. (1994), Adaptive Fuzzy Systems and Control -Design and Stability Analysis, New Jersey:Prentice-Hall.Google Scholar