Fuzzy logic learning algorithm for identifying inverse dynamics of a system

When designing a fuzzy logic controller, most of the time is spent in the developing the rule base. These rules are what describes how the fuzzy controller will work. This is usually a trial and error process. A starting set of rules are obtained and tried. The results are studied and the rules are adjusted. This process is repeated until the desired results are achieved. An automated process would greatly simplify the design process of a fuzzy logic controller. In this paper, a two level learning algorithm is studied and applied to obtain the inverse dynamics of a system. The learning algorithm allows the rules to be obtained from data collected from the system. The inverse dynamics is then implemented as a feedforward controller. To demonstrate the learning algorithm, simulation results are presented.