Adaptive Tube-based Nonlinear MPC for Ecological Autonomous Cruise Control of Plug-in Hybrid Electric Vehicles

TL;DR

This paper introduces an adaptive tube-based nonlinear MPC for autonomous cruise control in plug-in hybrid vehicles, combining robustness to uncertainties with real-time adaptivity to improve energy efficiency and constraint handling.

Contribution

It presents a novel dual-model AT-NMPC approach with a fast solver, enabling robust and adaptive control for PHEVs in real-time applications.

Findings

Successfully handles uncertainties and constraints in simulations.

Improves energy cost in PHEV cruise control.

Demonstrates real-time performance in hardware-in-loop tests.

Abstract

This paper proposes an adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of autonomous cruise control (ACC) systems. The proposed method utilizes two separate models to define the constrained receding horizon optimal control problem. A fixed nominal model is used to handle the problem constraints based on a robust tube-based approach. A separate adaptive model is used to define the objective function, which utilizes least square online parameter estimators for adaption. By having two separate models, this method takes into account uncertainties, modeling errors and delayed data in the design of the controller and guaranties robust constraint handling, while adapting to them to improve control performance. Furthermore, to be able implement the designed AT-NMPC in real-time, a Newton/GMRES fast solver is employed to solve the optimization problem. Simulations performed on a high-fidelity model of the baseline vehicle, the Toyota plug-in Prius, which is a plug-in hybrid electric vehicle (PHEV), show that the proposed controller is able to handle the defined constraints in the presence of uncertainty, while improving the energy cost of the trip. Moreover, the result of the hardware-in-loop experiment demonstrates the performance of the proposed controller in real time application.