Jt. Spooner et Km. Passino, STABLE ADAPTIVE-CONTROL USING FUZZY-SYSTEMS AND NEURAL NETWORKS, IEEE transactions on fuzzy systems, 4(3), 1996, pp. 339-359
Stable direct and indirect adaptive controllers are presented which us
e Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class
of neural networks to provide asymptotic tracking of a reference signa
l for a class of continuous-time nonlinear plants with poorly understo
od dynamics. The indirect adaptive scheme allows for the inclusion of
a priori knowledge about the plant dynamics in terms of exact mathemat
ical equations or linguistics while the direct adaptive scheme allows
for the incorporation of such a priori knowledge in specifying the con
troller, We prove that with or without such knowledge both adaptive sc
hemes can ''learn'' how to control the plant, provide for bounded inte
rnal signals, and achieve asymptotically stable tracking of a referenc
e input. In addition, for the direct adaptive scheme a technique is pr
esented in which linguistic knowledge of the inverse dynamics of the p
lant may be used to accelerate adaptation, The performance of the indi
rect and direct adaptive schemes is demonstrated through the longitudi
nal control of an automobile within an automated lane.