矿井搜救探测机器人驱动系统最小能耗建模与影响因素分析

计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (22): 77-84.

矿井搜救探测机器人驱动系统最小能耗建模与影响因素分析

苏学满1，2，孙丽丽1

1.安徽工程大学机械与汽车工程学院，安徽芜湖 241000
2.安徽工程大学机器人产业技术研究院，安徽芜湖 241000

出版日期:2015-11-15 发布日期:2015-11-16

Modeling for minimum energy consumption of mine rescue detection robot drive system and analysis on influence factors

SU Xueman1，2, SUN Lili1

1.School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China
2.Institute of Technology Robotics Industry, Anhui Polytechnic University, Wuhu, Anhui 241000, China

Online:2015-11-15 Published:2015-11-16

摘要/Abstract

摘要： 研究了矿井搜救探测机器人最小能耗影响因素。针对直流伺服电机和减速齿轮驱动下的移动机器人驱动系统，利用Hamiltonian函数与最小值原理求解出驱动系统的最优速度函数和最优控制电流函数，建立了非线性摩擦条件下的最小能耗模型。通过MATLAB仿真，分析了减速器效率[η]、负载、库仑摩擦力和粘性摩擦系数等因素对最小能耗的影响，确定了各参数对驱动系统最小能耗的影响规律。为矿井搜救探测机器人驱动系统消耗能量最小进行参数配置提供依据，对实现机器人节能降耗具有现实的意义。

关键词: 矿井搜救探测机器人, Hamiltonian, 最小值定理, 最小能耗

Abstract: Influence factors of minimum energy consumption of the mine rescue detection robot are studied. For driving system of the mobile robot driven by DC servo motor and reduction gear, the optimal velocity function and the optimal control current function of driving system are solved using the Hamiltonian function and the minimum theorem, and the minimum energy consumption model of nonlinear friction conditions is established. Through MATLAB simulation, the influences of the efficiency of the reducer, load, Coulomb friction and viscous friction coefficient on minimum energy consumption are analyzed, and the influence laws of various parameters on minimum energy consumption of the drive system are determined. The basis is provided for mine rescue detection robot servo motor driving system to configure parameters. It is practical significance to realize the energy saving.

Key words: mine rescue detection robot, Hamiltonian, minimum value theorem, minimum energy consumption

苏学满1，2，孙丽丽1. 矿井搜救探测机器人驱动系统最小能耗建模与影响因素分析[J]. 计算机工程与应用, 2015, 51(22): 77-84.

SU Xueman1，2, SUN Lili1. Modeling for minimum energy consumption of mine rescue detection robot drive system and analysis on influence factors[J]. Computer Engineering and Applications, 2015, 51(22): 77-84.