﻿ 基于机器视觉的智能小车转向系统滑模控制
 上海理工大学学报  2023, Vol. 45 Issue (6): 645-652 PDF

Sliding mode control of intelligent car steering based on machine vision
WANG Qiming, WAN Xuan, ZHANG Zhendong, SUN Tao, WU Guanghui
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Abstract: To solve the problem of poor tracking accuracy and slow response of intelligent car steering systems, a sliding mode control method of intelligent car steering system based on machine vision was proposed. Firstly, the intelligent car, which senses road information, was the research object. The principal analysis and mathematical modeling of the steering system were carried out. By analyzing the working process of the permanent-magnet direct current motor, the armature voltage balance equation and torque balance equation were listed, the transfer functions were obtained, and the transmission parameters were identified by experimental methods. Secondly, the sliding mode controller was designed for the above-mentioned controlled object, and its stability was proven. Matlab/Simulink simulation results verify the effectiveness of the sliding mode control algorithm. Finally, the combination of theoretical results and experimental verification shows that compared with PID（proportional integral differential）control, sliding mode control has better tracking accuracy and response speed at 20 Hz, the tracking accuracy is increased by 89.3%, and the system uncertainty can be overcome, especially for nonlinear systems.
Key words: steering differential control     intelligent car     sliding mode control     machine vision

1 智能小车转向系统建模 1.1 智能小车转向原理

 图 1 智能小车简化模型 Fig. 1 Simplified model of smart car

 $\frac{{{v_1}}}{{{R_1}}}{\text{ = }}\frac{{{v_2}}}{{{R_2}}}{\text{ = }}\frac{v}{R}$ (1)

1.2 电机传递函数模型搭建

 $\left\{ \begin{gathered} {u_{\rm{a}}} = {R_{\rm{a}}}{i_{\rm{a}}} + {L_{\rm{a}}}\frac{{{\rm{d}}{i_{\rm{a}}}}}{{{{\rm{d}}{{t}}}}} + \varepsilon \\ \varepsilon = {C_{\rm{e}}}\varPhi n \\ \end{gathered} \right.$ (5)
 $\left\{ \begin{gathered} J\frac{{{\rm{d}}n}}{{{\rm{d}}t}} = M - {M_{\rm{L}}} \\ M = {C_{\rm{M}}}\varPhi {i_{\rm{a}}} \\ \end{gathered} \right.$ (6)

 $\frac{{n\left( s \right)}}{{{u_{\rm{a}}}\left( s \right)}} = \frac{{1/{C_{\rm{e}}}\varPhi }}{{{T_{\rm{m}}}{T_{\rm{a}}}{s^2} + {T_{\rm{m}}}s + 1}}$ (9)

 $n(t) = \left[ {{\varPhi \mathord{\left/ {\vphantom {\varPhi {{T_{\rm{m}}}{C_{\rm{e}}}}}} \right. } {{T_{\rm{m}}}{C_{\rm{e}}}}}} \right]\left( {1 - {{\rm{e}}^{\frac{{ - t}}{{{T_{\rm{m}}}}}}}} \right) = K(1 - {{\rm{e}}^{\frac{{ - t}}{{{T_{\rm{m}}}}}}})$ (10)

 图 13 阶跃信号下PID控制和滑模控制跟踪性能和控制能量对比 Fig. 13 Comparison of tracking performance and control energy between PID control and sliding mode control under step signal

 图 14 0～20 Hz扫频信号下PID控制和滑模控制跟踪性能和控制能量对比 Fig. 14 Comparison of tracking performance and control energy between PID control and sliding mode control under 0～20 Hz sweep signal

4 结　论

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