Abstract:

The precision of speed tracking in the longitudinal motion control of intelligent vehicles is affected by multiple sources of disturbances， such as model mismatch and changes in external environments. In this paper， a longitudinal motion anti-disturbance control method that combines disturbance observation and Model Predictive Control （MPC） algorithm is accordingly proposed. Firstly， the relationship between the longitudinal acceleration of the vehicle and various forces is analyzed according to the longitudinal dynamics model of the vehicle， and then it is simplified into a particle motion model with multiple sources of disturbance and a model predictive controller is designed as the upper controller. Secondly， for the internal unmodeled dynamic disturbances and external random disturbances， a linear extended state observer （LESO） is designed to perform real-time estimation and compensation is implemented through a feedforward loop. The closed-loop stability of MPC and the convergence of LESO are analyzed， and finally a model predictive optimal regulation control law of disturbance compensation and state feedback is formed. Furthermore， in order to ensure efficient execution of the control strategy， a first-order anti-disturbance controller is proposed as the lower controller to convert the desired acceleration into engine torque， thereby realizing closed-loop control of the vehicle speed. Finally， the algorithm is deployed on a in-vehicle Microcontroller Unit （MCU） and tested on a real vehicle under multi-speeds and road conditions. The results show that the proposed strategy can quickly and accurately track the target vehicle speed， with excellent anti-disturbance ability.

Key words: intelligent vehicle, longitudinal speed tracking, anti-disturbance control, model predictive control, extended state observer