【摘要】 基于动力学模型的控制是改善机器人运动控制性能的有效方法，为了得到精确的动力学模型，需获得准确的机器人动力学参数，采用加权最小二乘法与遗传粒子群混合算法结合的辨识算法获得准确的动力学参数。关节摩擦影响动力学模型精度，传统库伦粘滞摩擦模型精度不高，引入一种新的连续摩擦模型，采用牛顿-欧拉法建立机器人动力学模型，采集机器人在激励轨迹运动下的数据，先采用加权最小二乘法辨识得到初始解，在初始解的基础上设定解的边界，分别采用遗传算法、粒子群算法、遗传粒子群混合算法辨识动力学参数，并于已有方法进行对比，结果表明遗传粒子群混合算法辨识动力学参数精度更高。最后，选取验证轨迹验证动力学参数的精度，结果表明辨识得到的动力学参数能够建立精确动力学模型。更多还原

【Abstract】 Dynamic model based control is an effective method to improve the performance of robot motion control.In order to obtain an accurate dynamic model, it is necessary to obtain accurate dynamic parameters of the robot.The identification algorithm combining weighted least squares method and genetic particle swarm optimization algorithm is used to obtain accurate dynamic parameters.Joint friction affects the accuracy of dynamic model.The accuracy of traditional Coulomb viscous friction model is not high.A new continuous friction model is introduced.The Newton Euler method is used to establish the robot dynamic model.Collect data of robot under excitation track motion.First, the weighted least square method is used to identify the initial solution.On the basis of the initial solution, the boundary of the solution is set.The genetic algorithm Particle swarm optimization algorithm and genetic particle swarm optimization hybrid algorithm are used to identify dynamic parameters, and compared with existing methods, the results show that genetic particle swarm optimization hybrid algorithm has higher accuracy in identifying dynamic parameters.Finally, the accuracy of the dynamic parameters is verified by selecting the verification trajectory.The results show that the identified dynamic parameters can establish an accurate dynamic model.更多还原