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In this paper the application of Virtual Reference Feedback Tuning (VRFT) forcontrol of nonlinear systems with regulators defined by Echo State Networks(ESN) and Long Short Term Memory (LSTM) networks is investigated. Thecapability of this class of regulators of constraining the control variable ispointed out and an advanced control scheme that allows to achieve zerosteady-state error is presented. The developed algorithms are validated on abenchmark example that consists of an electronic throttle body (ETB).

Advanced control based on Recurrent Neural Networks learned using  Virtual Reference Feedback Tuning and application to an Electronic Throttle  Body (with supplementary material)