An Automatic Tuning MPC with Application to Ecological Cruise Control
Mohammad Abtahi Mahdis Rabbani Shima Nazari
Model predictive control (MPC) is a powerful tool for planning and
controlling dynamical systems due to its capacity for handling constraints and
taking advantage of preview information. Nevertheless, MPC performance is
highly dependent on the choice of cost function tuning parameters. In this
work, we demonstrate an approach for online automatic tuning of an MPC
controller with an example application to an ecological cruise control system
that saves fuel by using a preview of road grade. We solve the global fuel
consumption minimization problem offline using dynamic programming and find the
corresponding MPC cost function by solving the inverse optimization problem. A
neural network fitted to these offline results is used to generate the desired
MPC cost function weight during online operation. The effectiveness of the
proposed approach is verified in simulation for different road geometries.