Model Predictive Control when utilizing LSTM as dynamic models

Abstract

The prediction model is the most important part of an MPC strategy. The accuracy of such a model influences the quality of predictions and control performance of the algorithm. In some practical cases, a model based on physical equations is not available, or is not easy to get all parameters, or its complexity could affect the real-time computation of the control signal. For this reason, the use of black-box models within a MPC framework becomes attractive, since to fit such models only input and output data are needed. Questions like: “Is it possible to use LSTM’s as predictors?” , “How to implement it?” , “What is the best way to compute derivatives?” , “Which solvers and tools are recommendable?” , “How to ensure the real-time capability?” are discussed in this work.