A systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability is introduced. The design is based on stability and hierarchy of identification and control. The fuzzy rule-base is stored in a fuzzy hypercube and the fuzzy control action is computed via a fuzzy inference mechanism. Initial conditions for the elements of a fuzzy hypercube are obtained by an offline fuzzy clustering mechanism with large-grain uncertainty. Two fuzzy algorithms are developed: the first one is a fuzzy identification-learning algorithm and the second is a fuzzy control-inferencing algorithm. The fuzzy identification-learning algorithm updates the membership functions on the action side of the rules and the fuzzy control-inferencing algorithm calculates fuzzy control data. This approach guarantees the stability, convergence, and robustness of the closed-loop feedback system. >