基于大数据聚类分析的移动机器人偏摆力矩控制系统设计

Design of mobile robot yaw moment control system based on big data clustering analysis

摘要:

移动机器人在不同类型的运动状态具有不同的运动特性,由于姿态的不断变化,系统更容易受到偏摆力矩的影响,出现姿态失衡的问题。为此,利用大数据聚类分析技术,优化设计移动机器人偏摆力矩控制系统。改装移动机器人姿态传感器和力矩传感器,加设大数据聚类分析处理器,调整力矩控制器内部组成结构,从电气设计和机械结构两个方面,添加抗干扰元件,实现硬件系统的优化设计。通过运动数据采集、特征提取和距离度量等步骤,在大数据聚类分析技术的支持下,识别移动机器人不同的运动状态。根据不同运动状态下移动机器人的标准运动参数,确定偏摆力矩控制目标,通过与测量偏摆力矩的比对,计算力矩控制量,实现系统的偏摆力矩控制功能,提高控制的稳定性。通过系统测试得出结论:与传统控制系统相比,优化设计系统的偏摆力矩控制误差减小约0.3N·m,在系统控制作用下移动机器人的稳定系数明显提升。

Abstract:

Mobile robots have different motion characteristics in different types of motion states. Due to the constantly changing posture, the system is more susceptible to the influence of yaw moment, resulting in posture imbalance. To this end, big data clustering analysis technology is utilized to optimize the design of a mobile robot yaw moment control system. Modify the posture sensor and torque sensor of the mobile robot, add a big data clustering analysis processor, adjust the internal composition structure of the torque controller, and add anti-interference components from both electrical design and mechanical structure aspects to achieve optimized design of the hardware system. Through steps such as motion data collection, feature extraction, and distance measurement, supported by big data clustering analysis technology, different motion states of mobile robots are identified. Based on the standard motion parameters of the mobile robot under different motion states, determine the control target of the yaw moment. By comparing it with the measured yaw moment, calculate the torque control quantity to achieve the yaw moment control function of the system and improve the stability of the control. The conclusion drawn from system testing is that compared with traditional control systems, the optimized design system reduces the control error of yaw moment by about 0.3N · m, and significantly improves the stability coefficient of the mobile robot under system control.