A method for design of a hybrid neuro-fuzzy control system based on behavior modeling

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea leads to modeling of a plant with unknown structure by defining several types of dynamic behavior, such as “oscillation,” “overdamping,” “underdamping,” and so forth. On the basis of dynamic behavior classification, a new method is presented for design of a neurofuzzy control system in two steps: First, we model a plant with unknown structure by choosing a set of simplified systems with equivalent behavior as “templates” to optimize their fuzzy controllers off-line. Second, we use an algorithm for system identification to perceive dynamic behavior and a neural network to adapt fuzzy logic controllers by matching the “templates” online. The main advantage of this method is that convergence problem can be avoided during adaptation process. Finally, the proposed method is used to design neuro-fuzzy controllers for a two-link manipulator.