Energy-Optimal Adaptive Control Based on Model Predictive Control

被引：4
|
作者：
Li, Yuxi [1 ]
Hao, Gang [1 ,2 ]
机构：
[1] Heilongjiang Univ, Sch Elect Engn, Harbin 150080, Peoples R China
[2] Key Lab Informat Fus Estimat & Detect, Harbin 150080, Peoples R China
来源：
SENSORS | 2023年 / 23卷 / 09期
关键词：
model predictive control (MPC); cubature Kalman filter; energy-optimal cruise control; artificial neural network; CRUISE CONTROL; DESIGN; ALGORITHM;
D O I：
10.3390/s23094568
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN). The proposed MPC improves safety and tracking performance while further reducing energy consumption. The final simulation results show that the proposed algorithm has a stronger energy-saving capability compared to previous studies and always maintains an appropriate relative distance and relative speed to the vehicle in front, verifying the effectiveness of the proposed algorithm.
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页数：16
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