Path planning and path tracking control for autonomous vehicle based on MPC with adaptive dual-horizon-parameters

doi:10.3969/j.issn.1674-8484.2021.04.012

Abstract

Abstract:

This paper designed a novel obstacle avoidance function by using the nonlinear model predictive control (MPC) algorithm to solve the problem of excessive obstacle avoidance. Established a series of comprehensive evaluation index of vehicle path tracking performance, obtained the optimal prediction time domain and the control time domain parameters, and designed the adaptive dual time domain MPC parameters path tracking controller. A joint simulation platform integrating the planning layer and the control layer was built for simulation. The results show that under the condition of 180 obstacle points, the obstacle avoidance function can avoid excessive obstacle avoidance, and the calculation time only increases by 0.294 ms; The maximum lateral error deviation is reduced by 0.169 m with the maximum yaw rate being reduced by 3.196 (°)/s when adopting the integrated structure of local obstacle avoidance path planning and path tracking control, in multiple static obstacle scenes and dynamic obstacle scenes, at the vehicle speed of 65 km/h.

Key words: autonomous vehicles, model predictive control (MPC), obstacle avoidance, local path planning, path tracking controller

CLC Number:

U461.91

LI Yaohua, FAN Jikang, LIU Yang, HE Jie, LI Zetian, PAN Shaofei. Path planning and path tracking control for autonomous vehicle based on MPC with adaptive dual-horizon-parameters[J]. Journal of Automotive Safety and Energy, 2021, 12(4): 528-539.

Figures/Tables 23

References 21

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整车质量,m	1.723 t
长×宽,L×B	4.893 m × 1.862 m
前轴到质心距离,a	1.232 m
后轴到质心距离,b	1.468 m
前轮侧偏刚度,C_f	66.9 kN·rad^-1
前轮侧偏刚度,C_r	62.7 kN·rad^-1
绕z轴转动惯量,I_z	3 234 t·m²

整车质量,m	1.723 t
长×宽,L×B	4.893 m × 1.862 m
前轴到质心距离,a	1.232 m
后轴到质心距离,b	1.468 m
前轮侧偏刚度,C_f	66.9 kN·rad^-1
前轮侧偏刚度,C_r	62.7 kN·rad^-1
绕z轴转动惯量,I_z	3 234 t·m²

采样周期,T_c	20 ms
权重矩阵,Q	diag[2 000, 10 000]
权重矩阵,R	[5×10⁵]
权重因子,ρ	1 000
控制量约束	[-10°, 10°]
控制增量约束	[-0.85°, 0.85°]

采样周期,T_c	20 ms
权重矩阵,Q	diag[2 000, 10 000]
权重矩阵,R	[5×10⁵]
权重因子,ρ	1 000
控制量约束	[-10°, 10°]
控制增量约束	[-0.85°, 0.85°]

v / (km﹒h^-1)	N_p	N_c	E_dmax/mm	E_dm/mm	E_φ_m/(°)	β_max/(°)	ω_max/ [(°)﹒s^-1)]
30	17	1	72	17	0.761	1.380	13.181
30	19	16	67	17	0.746	1.349	10.870
50	22	4	108	30	0.546	0.497	18.890
70	33	2	215	55	0.541	1.337	19.190
70	40	1	645	125	1.210	0.633	13.602