基于GMS摩擦模型的机器人低速运动研究  

Research of robotic low-speed motion based on GMS friction model

作　　者：靳兴来[1] 朱世强[1] 吴文祥[1] 

机构地区：[1]浙江大学流体动力与机电系统国家重点实验室,浙江杭州310027

出　　处：《传感器与微系统》2014年第1期38-41,共4页Transducer and Microsystem Technologies

基　　金：浙江省自然科学基金资助项目(Y1100693)

摘　　要：为了降低摩擦对机器人低速运动性能的干扰,引入了能够全面描述低速阶段摩擦力特性的GMS摩擦模型,并且利用遗传算法进行参数辨识,采用低通滤波器滤除高频噪声。为了验证该模型的有效性,设计了以机械手关节作为实验平台的轨迹跟踪实验,将模型作为前馈补偿引入系统。通过与常用的Stribeck模型补偿的对比,证明了GMS模型能更完善地描述摩擦力的特性,能进一步改善机械手的轨迹跟踪性能。In order to reduce influence of friction on properties of robotic low-speed motion, generalized Maxwell- slip （GMS） model is introduced which can describe characteristic of friction in low-speed stage. Parameters identification is realized by genetic algorithm and low-pass filter is implemented to filter high-frequency noise. To validate the effectiveness of this model, design trajectory tracking experiments on the manipulator, and GMS model is involved as feedforward compensation. Results of experiments show that GMS model can well describe the variety of friction in low-speed motion and further improve robotic tracking performance compared to widely used Stribeck friction model.

关 键 词：GMS摩擦模型 遗传算法 低通滤波 前馈补偿 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]