Investigation on multi-objective following control algorithm for vehicle adaptive cruise control under cruise state

The following control problem is a challenging issue in vehicle adaptive cruise control. In the control process, multiple objectives need to be considered while ensuring safety. To comprehensively study and evaluate the following control algorithms, this paper establishes a following model, vehicle model, and energy consumption model. After verifying the accuracy of the models, corresponding proportion-integral-derivative (PID), model predictive control (MPC), and adaptive dynamic programing (ADP) algorithms are proposed based on the models. The three algorithms are integrated into a vehicle using Simulink, and real vehicle experiments are conducted. The results show that the MPC algorithm achieves the best control performance and exhibits superior disturbance rejection capabilities. The control performance of the ADP and PID algorithms is ranked second. However, the MPC algorithm has very limited computational margin, which may restrict the use of additional computational resources. Therefore, in situations where computational resources are relatively scarce and strict control performance requirements are not imposed, the ADP algorithm can be used as a substitute for the MPC algorithm. The traditional PID algorithm exhibits the best real-time performance but significantly weaker control performance and disturbance rejection capabilities compared to the other two algorithms.