Introducing a Deep Neural Network-based Model Predictive Control
  Framework for Rapid Controller Implementation
David C. Gordon Alexander Winkler Julian Bedei Patrick Schaber Jakob Andert Charles R. Koch
Model Predictive Control (MPC) provides an optimal control solution based on
a cost function while allowing for the implementation of process constraints.
As a model-based optimal control technique, the performance of MPC strongly
depends on the model used where a trade-off between model computation time and
prediction performance exists. One solution is the integration of MPC with a
machine learning (ML) based process model which are quick to evaluate online.
This work presents the experimental implementation of a deep neural network
(DNN) based nonlinear MPC for Homogeneous Charge Compression Ignition (HCCI)
combustion control. The DNN model consists of a Long Short-Term Memory (LSTM)
network surrounded by fully connected layers which was trained using
experimental engine data and showed acceptable prediction performance with
under 5% error for all outputs. Using this model, the MPC is designed to track
the Indicated Mean Effective Pressure (IMEP) and combustion phasing
trajectories, while minimizing several parameters. Using the acados software
package to enable the real-time implementation of the MPC on an ARM Cortex A72,
the optimization calculations are completed within 1.4 ms. The external A72
processor is integrated with the prototyping engine controller using a UDP
connection allowing for rapid experimental deployment of the NMPC. The IMEP
trajectory following of the developed controller was excellent, with a
root-mean-square error of 0.133 bar, in addition to observing process
constraints.