Advanced control based on Recurrent Neural Networks learned using
Virtual Reference Feedback Tuning and application to an Electronic Throttle
Body (with supplementary material)
In this paper the application of Virtual Reference Feedback Tuning (VRFT) for
control of nonlinear systems with regulators defined by Echo State Networks
(ESN) 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 an advanced control scheme that allows to achieve zero
steady-state error is presented. The developed algorithms are validated on a
benchmark example that consists of an electronic throttle body (ETB)