Command-filter-adaptive-based lateral motion control for autonomous vehicle

Human-machine collaborative (HMC) torque control allows the drivers to participate in driving and cooperate to complete driving tasks. However, the driver's torque input and unknown disturbances encountered by the vehicle will adversely affect the control of autonomous vehicles. The problems of ''explosion of complexity appear when the adaptive backstepping method is used to design the steering control strategy. To solve the above problems, a steering control strategy based on command filter adaptive torque control is proposed. Firstly, in order to describe the dynamic response of the vehicle and the maneuvering behavior of the driver, a human-vehicle-road model with steering torque as input is established. Then, command filtering adaptive control (CFAC) is used to design the steering control strategy of HMC, and Lyapunov theory is used to analyze the stability of the control system. To reduce the uncertainty range of the system and guarantee stability under disturbances, the unknown steering load boundary and driver's torque input are estimated. Finally, simulation and hardware-in-the-loop experiments verify the effectiveness of the proposed steering control strategy based on torque control. The experimental results show that the path tracking error converges to zero rapidly even when the HMC driving encounters disturbances.

Automated summary

Human-machine collaborative torque control allows drivers to participate in driving and complete driving tasks together, but the driver's torque input and unknown disturbances can negatively affect autonomous vehicle control. This study proposes a steering control strategy based on command filter adaptive torque control to address these issues. By estimating the unknown steering load boundary and driver's torque input, the uncertainty range of the system is reduced and stability is guaranteed.