STABLE ADAPTIVE-CONTROL USING FUZZY-SYSTEMS AND NEURAL NETWORKS

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.