Neural networks in virtual reference tuning

Abstract:

This paper discusses the application of the virtual reference tuning (VRT) techniques to tune neural controllers from batch inputoutput 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. © 2011 Elsevier Ltd. All rights reserved.

Año de publicación:

2011

Keywords:

• Model reference control
• Direct controller design
• Virtual reference feedback tuning
• Back propagation through time
• Neural networks
• Data-based controller tuning

Fuente:

scopus