主办:陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊:1976年

Automobile Applied Technology ›› 2023, Vol. 48 ›› Issue (9): 59-65.DOI: 10.16638/j.cnki.1671-7988.2023.09.012

• Intelligent Connected Vehicle • Previous Articles    

Longitudinal Acceleration Control of Intelligent Vehicle Following Autonomous Driving Based on MFAPC

TAN Yuhang   

  1. School of Construction Machinery, Chang'an University
  • Online:2023-05-15 Published:2023-05-15
  • Contact: TAN Yuhang

基于 MFAPC 的智能车辆跟随式自动驾驶 纵向加速度控制算法

谭宇航   

  1. 长安大学 工程机械学院
  • 通讯作者: 谭宇航
  • 作者简介:谭宇航(1998—),男,硕士研究生,研究方向为车辆动力学控制,E-mail:583215692@qq.com。

Abstract: Aiming at the problems of nonlinearity and external interference in the longitudinal dynamics of intelligent driving vehicles, a model-free adaptive predictive control (MFAPC) vehicle longitudinal acceleration control algorithm based on compact format is proposed, and simulation is carried out. Feed-forward control improves response and compensates for the nonlinear characteristics of the system; The MFAPC algorithm does not rely on the exact model of the controlled system, but only utilizes the input/output data of the controlled system. Feed-forward control works in synergy with MFAPC feedback control to enable fast and accurate tracking of desired acceleration. Finally, by comparing with the proportion integration differentiation (PID) control algorithm, the proposed control method can still have good performance in the case of external interference.

Key words: Longitudinal dynamics; Follow-up autonomous driving; Acceleration control; MFAPC

摘要: 针对智能驾驶车辆纵向动力学存在非线性、外界干扰等问题,提出一种基于紧格式的 无模型自适应预测控制(MFAPC)车辆纵向加速度控制算法,并进行了仿真验证。前馈控制 可以提高响应,并补偿系统的非线性特性;MFAPC 算法不依赖被控系统的精确模型,仅利用 了被控系统的输入/输出数据。前馈控制与 MFAPC 反馈控制协同作用,实现了对期望加速度 快速、精确地跟踪。最后,通过与比例-积分-微分(PID)控制算法进行比较,所提出的控制 方法能够在有外界干扰的情况下,仍具有较好的性能表现。

关键词: 纵向动力学;跟随式自动驾驶;加速度控制;MFAPC