Stable adaptive fuzzy control for an automated highway system

Abstract

Recently, stable direct and indirect adaptive controllers have been presented which use Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous-time nonlinear plants with poorly understood dynamics. The indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics while the direct adaptive scheme allows for the incorporation of such a priori knowledge in specifying the controller. In this paper, the performance of these indirect and direct adaptive schemes is demonstrated through the longitudinal control of an automobile in an automated highway system.