Intelligent PI control based on the ultra-local model and Kalman filter for vehicle yaw-rate control

Abstract

This paper proposes an intelligent proportional integral (PI) control based on an ultra-local model and a Kalman filter with automatic parameter tuning for vehicle yaw-rate control. A good vehicle yaw-rate controller is essential for autonomous driving. The traditional PI controller proposed in the conventional method cannot realize the desired yaw-rate response because the vehicle velocity yaw-rate characteristics change with vehicle velocity. Although an intelligent model-free PI control can improve the yaw-rate response, it does not account for the measurement noise, so the noise amplified by feedback control is added to the control input. This problem is crucial because a large control input velocity (i.e. a high steering-angle velocity) may cause wheel damage. A Kalman filter was introduced to conventional intelligent PI control to address this problem. In addition, we automatically tune the design parameters of the Kalman filter by Bayesian optimization. The effectiveness of the proposed method was investigated in a vehicle simulator. The experimental results confirm higher control performance of the proposed method than of conventional methods in noisy cases.

Keywords

• pid control
• model-free control
• autonomous driving
• kalman filter
• bayesian optimization