Recurrent Neural Network controllers learned using Virtual Reference Feedback Tuning with application to an Electronic Throttle Body

In this paper the application of Virtual Reference Feedback Tuning (VRFT) for control of nonlinear systems with regulators defined by Echo State Networks (ESNs) and Long Short Term Memory (LSTM) networks is investigated. The capability of this class of regulators of constraining the control variable is pointed out and a control scheme that allows to achieve zero steady-state error is presented. The developed scheme is validated on a benchmark example that consists of an Electronic Throttle Body (ETB).