RSFDGrC 1999: New Directions in Rough Sets, Data Mining, and Granular-Soft Computing pp 239-247 | Cite as
Fuzzy Control of Nonlinear Systems Using Nonlinearized Parameterization
Abstract
In the most of adaptive fuzzy control schemes presented so far still only the parameters (weights of each rule’s consequent), which appear linearly in the radial basis function (RBF) expansion, were tuned. The major disadvantage is that the precision of the parameterized fuzzy approximator can not be guaranteed. Consequently, the control performance has been influenced. In this paper, we not only tune the weighting parameters but tune the variances which appears nonlinearly in the RBF to reduce the approximation error and improve control performance, using a lemma by Annaswamy et al. (1998) which was named as concave/convex parameterization. Global boundedness of the overall adaptive system and tracking to within precision are established with the proposed adaptive controller.
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