  Physics-informed deep learning is a popular trend in the modeling and control
of dynamical systems. This paper presents a novel method for rapid online
identification of vehicle cornering stiffness coefficient, a crucial parameter
in vehicle stability control models and control algorithms. The new method
enables designers to rapidly identify the vehicle front and rear cornering
stiffness parameters so that the controller reference gains can be re-adjusted
under varying road and vehicle conditions to improve the reference tracking
performance of the control system during operation. The proposed method based
on vehicle model-based deep learning is compared to other alternatives such as
traditional neural network training and identification, and Pacejka model
estimation with regression. Our initial findings show that, in comparison to
these classical methods, high fidelity estimations can be done with much
smaller data sets simple enough to be obtained from a lane-changing or vehicle
overtake maneuver. In order to conduct experiments, and collect sensor data, a
custom-built 1:8 scaled test vehicle platform is used real-time wireless
networking capabilities. The proposed method is applicable to predict derived
vehicle parameters such as the understeering coefficient so it can be used in
parallel with conventional MIMO controllers. Our $H_{\infty}$ yaw rate
regulation controller test results show that the reference gains updated with
the proposed online estimation method improve the tracking performance in both
simulations and vehicle experiments.
