HYBRIDIZATION OF NEURAL AND FUZZY-SYSTEMS BY A MULTI AGENT ARCHITECTURE FOR MOTOR GEARBOX CONTROL

In this paper a hybrid system for motor control on testbeds, consistin g of neural networks with a self-organizing process state detection an d fuzzy rulebases, is proposed. The basic mechanism used for hybridiza tion is a multiagent system composed from loosely interconnected subsy stems for the different control tasks to be accomplished. The major ai ms taken into account are: using standard - and approved - subsystems, realize an easily expandable system, which can handle the exchange or even failure of a component. The proposed system is implemented as a first stage using a simple motor and car simulation. First results sho w the system's capability to control the car simulation precisely foll owing a given speed profile using knowledge acquisition from the fuzzy system to the neural network. Published by Elsevier Science B.V.