APPLICATION OF NEURAL NETWORKS TO VIRTUAL REFERENCE FEEDBACK TUNING CONTROLLER DESIGN

This paper discusses the application of the Virtual Reference Feedback Tuning technique to tune neural controllers from experimental data, by particularising nonlinear VRFT and suitably computing gradients backpropagating in time. Alternative block diagrams with extra inputs have also been considered. The neural approach to VRFT is compared to the linear one in a simulated crane example.