An Automatic Tuning MPC with Application to Ecological Cruise Control
This work addresses the tuning challenge in MPC for fuel-saving cruise control, but it is incremental as it builds on existing inverse optimization and neural network techniques.
The paper tackles the problem of MPC performance dependence on cost function tuning by proposing an online automatic tuning method, applied to ecological cruise control to save fuel using road grade preview, achieving verified effectiveness in simulation across different road geometries.
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.