Neural networks in virtual reference tuning

This paper discusses the application of the virtual reference tuning (VRT) techniques to tune neural controllers from batch input–output data, by particularising nonlinear VRT and suitably computing gradients backpropagating in time. The flexibility of gradient computation with neural networks also allows alternative block diagrams with extra inputs to be considered. The neural approach to VRT in a closed-loop setup is compared to the linear VRFT one in a simulated crane example.