Energy Efficiency Oriented Robust Model Predictive Stability Control for Autonomous Electric Vehicles

The four-wheel independent steering and drive autonomous vehicle is a typical over-actuated system. The complexity of controlling it is increasing with the number of actuators. Since the model-based approach can solve the constrained multiple output problem, it is mostly utilized in the existing works. However, they usually investigate a single objective optimization, while employing simplified prediction models to relieve computational burdens. In this case, the robustness of the controller will inevitably suffer from model mismatch, which makes it hard to fulfill the various demands of autonomous driving. This work proposes a multi-objective control framework, which optimizes stability and energy efficiency simultaneously. Furthermore, robust model predictive control is introduced to address the model mismatch. Compared with the state-of-the-art, the effectiveness of the proposed approach has been validated by hardware-in-the-loop tests. Under the double lane change Maneuver, the longitudinal speed is maintained 1.7% higher. The vehicle stability is enhanced, while the motor energy loss and tire slip energy are reduced by 23.3% and 8.3%, respectively.