Abstract
By using a nonlinear parametric fuzzy identifier, an adaptive backstepping controller is proposed for a class of nonlinear systems. The nonlinear parametric fuzzy identifier is capable of automatically learning its membership functions. Since the fuzzy identifier is highly nonlinear, the derivative computation burden is enormous. Thus, this paper uses an estimation technique to effectively alleviate the derivative computation burden, and demonstrates the applicability of the proposed scheme by using computer simulation.
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Leu, YG., Lin, JY. (2009). Adaptive Backstepping Fuzzy Control for a Class of Nonlinear Systems. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_127
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DOI: https://doi.org/10.1007/978-3-642-01510-6_127
Publisher Name: Springer, Berlin, Heidelberg
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