Multirate Estimation and Control of Body Slip Angle for Electric Vehicles Based on Onboard Vision System

A new method for vehicle body-slip-angle estimation using nontraditional sensor configuration and system model is proposed, which enables robust estimation against vehicle parameter uncertainties. In this approach, a linear vehicle bicycle model is augmented with a simple visual model. As the visual model contains few uncertain parameters and increases the observer´s design freedom, the combined model-based estimator provides more accurate estimation result compared with the traditional bicycle-model-based one. However, two issues are raised by the combined vehicle and vision models: 1) image processing introduces delay in the visual measurements, and 2) the sampling time of a normal camera is much longer than that of other onboard sensors. For electric vehicles, the control period of motors is much shorter than the sampling time of a normal camera. Considering the aforementioned delay and multirate problems, a multirate Kalman filter with intersample compensation is designed, and the estimation performance can be improved during the sampling intervals of the vision system. Then, a two-degree-of-freedom controller is designed using the estimated body slip angle as feedback for reference tracking. With the proposed multirate estimator, the controller achieves better tracking performance than the singlerate method. The effectiveness of the proposed estimator and controller is demonstrated by both simulations and experiments.