  With the advantages of high modeling accuracy and large bandwidth, recurrent
neural network (RNN) based inversion model control has been proposed for output
tracking. However, some issues still need to be addressed when using the
RNN-based inversion model. First, with limited number of parameters in RNN, it
cannot model the low-frequency dynamics accurately, thus an extra linear model
has been used, which can become an interference for tracking control at high
frequencies. Moreover, the control speed and the RNN modeling accuracy cannot
be improved simultaneously as the control sampling speed is restricted by the
length of the RNN training set. Therefore, this article focuses on addressing
these limitations of RNN-based inversion model control. Specifically, a novel
modeling method is proposed to incorporate the linear model in a way that it
does not affect the existing high-frequency control performance achieved by
RNN. Additionally, an interpolation method is proposed to double the sampling
frequency (compared to the RNN training sampling frequency). Analysis on the
stability issues which may arise when the proposed new model is used for
predictive control is presented along with the instructions on determining the
parameters for ensuring the closed-loop stability. Finally, the proposed
approach is demonstrated on a commercial piezo actuator, and the experiment
results show that the tracking performances can be significantly improved.
