Nearly-optimal Explicit MPC-based Reference Governors with Long Prediction Horizons Generated with Machine Learning

The paper shows a procedure for constructing an approximated explicit form of the MPC-based reference governor. MPC-based reference governors are often setup up with long prediction horizons with a significant number of constraints, which forbids using conventional parametric optimisation to obtain the explicit solution. This paper explores the approach of mimicking the behaviour of the MPC-based reference governor with a neural network. The paper shows methods that ensure point-wise satisfaction of process constraints during neural network training. A demonstration using a well-known MIMO process is offered to evaluate control performance.