LSTM Replaces MIMO MPC

Model Predictive Control (MPC) is applied online with real-time optimizers that coordinate control moves. This case study demonstrates training and replacement of the MPC with an LSTM network. Two prior case studies demonstrate this same approach with an LSTM Network Replacing PID control and SISO (Single Input, Single Output) MPC. In this case study, the LSTM network is trained from a 2x2 MIMO MPC (Single Input Single Output, Model Predictive Control). LSTM (Long Short Term Memory) networks are a special type of RNN (Recurrent Neural Network) that is structured to remember and predict based on long-term dependencies that are trained with time-series data.

Deployment Solution with LSTM Network
Training and testing are often performed once on a dedicated computing platform. The trained model is then packaged for another computing system for predictions as a deployed machine learning solution. The final step of this exercise is to deploy the LSTM controller, independent of the training program. The controller (configuration in lstm_control.pkl and model in lstm_control.h5) can be deployed on embedded architectures without optimizers. Switch to use the TCLab hardware with tclab_hardware = True. Otherwise, the script uses the digital twin simulator.

Machine Learning for Engineers: https://apmonitor.com/pds
Dynamic Optimization: https://apmonitor.com/do
LSTM Replaces MIMO MPC Case Study: https://apmonitor.com/do/index.php/Ma...