不确定影响下智能车辆纵向干扰估计与运动控制

To mitigate the interference of external disturbances and environmental uncertainties and improve vehicle speed tracking accuracy, this study proposes a longitudinal motion controller that integrates a High Gain Extended State Observer (HGESO) with Model Predictive Control (MPC). First, the multi-source external uncertainties are consolidated into a stochastic time-varying resistance term in the speed control framework, which is estimated using the HGESO. This approach is combined with a nominal state-space model to enhance the description of vehicle longitudinal dynamics. Subsequently, an incremental MPC controller incorporating the estimated disturbance resistance is employed. This controller designs a multiobjective optimization function that simultaneously considers longitudinal speed tracking error, ride comfort, and energy consumption, ultimately solving for the optimal control input. Finally, precise calibration of the lower-level controller's mapping table is performed to ensure accurate output of throttle and brake commands, thereby enhancing the controller's real-time execution capability. Experimental results demonstrate significant improvements in speed tracking accuracy under challenging conditions: a 35%~61.54% enhancement is achieved on steep slopes, and a 26.3%~80.8% improvement is observed during continuous steering maneuvers. The proposed control strategy effectively eliminates the impact of external disturbances on vehicle longitudinal control.