Advanced predictive control for GRU and LSTM networks

Abstract

This article is concerned with Model Predictive Control (MPC) algorithms that use Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for prediction. For two benchmark processes, it is shown that the typical approach to MPC that hinges on successively linearized LSTM or GRU models do not give precise predictions and satisfactory control quality. The presented MPC control schemes utilize online advanced trajectory linearization, which yields simple quadratic optimization programs. It is shown that the discussed approaches give excellent prediction accuracy and control quality, very similar to that possible in MPC with full nonlinear prediction and nonlinear optimization done online. It is also demonstrated that the described MPC algorithms are a few times faster than the MPC method with nonlinear optimization. Moreover, the performance of MPC based on LSTM and GRU networks is compared, and simpler GRU networks are recommended.