Autonomous Vehicle Control Comparison

被引:6
作者
Banginwar, Pruthvi [1 ]
Sands, Timothy [2 ]
机构
[1] Cornell Univ, Dept Appl Engn Phys, Ithaca, NY 14853 USA
[2] Cornell Univ, Sibley Sch Mech & Aerosp Engn, Ithaca, NY 14853 USA
来源
VEHICLES | 2022年 / 4卷 / 04期
关键词
controller; simulink; Monte Carlo; Pontryagin; optimization; patching; filter;
D O I
10.3390/vehicles4040059
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Self-driving features rely upon autonomous control of vehicle kinetics, and this manuscript compares several disparate approaches to control predominant kinetics. Classical control using feedback of state position and velocities, open-loop optimal control, real-time optimal control, double-integrator patching filters with and without gain-tuning, and control law inversion patching filters accompanying velocity control are assessed in Simulink, and their performances are compared. Optimal controls are found via Pontryagin's method of optimization utilizing three necessary conditions: Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. It is found that real-time optimal control and control-law patching filter with velocity control incorporating optimization are the two best methods overall as judged in Monte Carlo analysis by means and standard deviations of position and rate errors and cost.
引用
收藏
页码:1109 / 1121
页数:13
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