摘要
由于关节机器人受摩擦、打滑等非线性因素的影响,传统比例-微分控制在解决高精度非线性控制问题时效果不理想。一种基于大脑情感学习控制器(BELC)的两关节机器人非线性运动控制方法被提出。首先,该方法将BELC应用于基于凯恩法的两关节机器人动力模型中,利用传统比例-微分控制方法设定感官输入信号和奖励信号的函数值。然后,针对BELC模型特点,设计了BELC学习速率的动态响应策略,使其能够依据关节机器人实际运动速度变化率而动态变化。最后,分别对传统比例-微分控制、BELC控制及改进后的BELC控制进行了仿真实验,实验结果表明改进后的基于BELC的两关节机器人能够有效抵抗不确定非线性因素对运动控制的影响,从而提高了系统控制精度和响应速度。
In view of the influence of nonlinear factors such as friction and slip,the traditional proportional-differential(PD)control is not ideal for high precision nonlinear control. A method is presented for nonlinear motion control of two joint robot based on brain emotional learning controller(BELC). Firstly,this method applies BELC to the dynamic model of two joint robot which is established by Kane method. The traditional PD control method is used to set the function values of the sensory input signal and the reward signal. Then,according to the characteristics of BELC model,the dynamic response strategy of BELC learning rate is designed. It can be dynamically changed according to the actual speed change rate of the joint robot.Finally,the traditional PD control,the BELC control and the improved BELC control are simulated,the experimental results show that the optimized BELC of two joint robot can effectively resist the influence of uncertain nonlinear factors on the motion control,thus the system control precision and response speed are improved.
作者
赵国新
宋玉宝
王安
王展鹏
ZHAO Guo-xin;SONG Yu-bao;WANG An;WANG Zhan-peng(College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China;College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China)
出处
《机械设计与制造》
北大核心
2019年第8期139-141,145,共4页
Machinery Design & Manufacture
基金
国家自然科学基金(51405023)
关键词
关节机器人
大脑情感学习
运动控制
学习速率
建模仿真
Joint Robot
Brain Emotion Learning
Motion Control
Learning Rate
Modeling and Simulation
