MPC vehicle trajectory tracking and control algorithm based on adaptive feedback
In order to solve the problem of large steady-state error when trajectory tracking controller is designed by model predictive control during high-speed motion under oversimplified vehicle kinematic model,a prospective error feedback mechanism is introduced by using neural network PID controller,and a model predictive trajectory tracking and control method based on neural network PID feedback is proposed. By calculating the average error of T time domain of prediction,using the adaptive characteristics of neural network PID,integrating into the model predictive control algorithm,in order to improve the adaptive ability of different trajectory curve tracking and control and reduce the lateral steady-state error. The experimental results show that the improved algorithm can effectively make the steady-state error closer to zero,and both the average error and the maximum error are reduced by more than 30％. The maximum error is reduced by 52.99％ and the average error is reduced by 35.78％ when using the real trajectory with large curvature to test.
万方数据
维普
