Selflearning fuzzy controller with smooth transfer characteristic and guaranteed convergence

The paper presents a user-friendly way to design a smooth nonlinear control surface. The method can be seen as a fuzzy control design tool, but it can also be seen in the context of neural networks, B-spline basis functions, or simply as a tool for setting up an input-output map. The design process is composed of two steps. First, an expert knowledge is used in a rule based manner to set up the main structure of the control surface. Second, an automatic learning algorithm is used to improve the control surface and to compensate for slowly time varying effects, like the aging of the system. Applications are mentioned and the convergence of the learning algorithm is proved under real world conditions