A supervisory fuzzy neural network control system for tracking periodic inputs

A supervisory fuzzy neural network (FNN) control system is designed to trac k periodic reference inputs in this study, The control system is composed o f a permanent magnet CPM) synchronous servo motor drive with a supervisory FNN position controller, The supervisory FNN controller comprises a supervi sory controller, which is designed to stabilize the system states around a defined bound region and an FNN sliding-mode controller, which combines the advantages of the sliding-mode control with robust characteristics and the FNN with on-line learning ability. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and e xperimental results show that the proposed control system is robust with re gard to plant parameter variations and external load disturbance. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.