An Automatic Tuning MPC with Application to Ecological Cruise Control

TL;DR

This paper presents an online automatic tuning method for MPC controllers, using inverse optimization and neural networks, demonstrated on an ecological cruise control system to optimize fuel efficiency with preview information.

Contribution

It introduces a novel approach combining offline inverse optimization and neural networks for real-time MPC parameter tuning in ecological cruise control.

Findings

The method reduces fuel consumption in simulations across various road geometries.

Neural network-based tuning adapts MPC parameters effectively during online operation.

The approach improves control performance without manual parameter adjustments.

Abstract

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.