Data-driven design of model-free control for reference model tracking based on an ultra-local model: Application to vehicle yaw rate control

Lateral vehicle dynamics control is important for autonomous driving. This paper presents a data-driven design of model-referenced model-free control (DD-MR-MFC) based on an ultra-local model for vehicle yaw rate control. The characteristics of lateral vehicle dynamics systems depend on vehicle velocities and weights. For this system, fixed proportional–integral–derivative (PID) controllers cannot provide the desired control performance. Additionally, although model-based control can be applied to lateral vehicle dynamics, the modeling process is time-consuming. To efficiently design controllers that can realize the desired performance, we adopt a model-free approach. In this study, the control law of practical MR-MFC is derived by extending the traditional MFC based on an ultra-local model and using a data-driven design method. The MFC approach can be applied to nonlinear systems with few parameters, and the data-driven method provides optimized parameters from single-experiment time-series data without the need for repeated experiments and system model to be controlled. Additionally, the processing cost is considerably low because the controller parameter can be obtained using least-square methods. The effectiveness of the proposed method is verified using a multibody vehicle simulator. The yaw rate tracking performance is examined under different velocities and loads. Results showed that the root-mean-square error of the proposed method is approximately 1/100th of that when using a fixed PID controller optimized using a data-driven method.