基于混合遗传算法的机器人改进摩擦模型辨识

为了解决机器人运动过程中库伦+黏性摩擦模型不能真实反映关节摩擦的非线性特性的问题，采用改进Stribeck摩擦模型对机器人关节摩擦进行建模，提出基于混合遗传算法与余弦轨迹的模型参数辨识方法. 采用不同的余弦轨迹对机器人关节进行激励，利用已知的机器人动力学方程确定关节摩擦力矩，从而建立机器人关节速度与关节摩擦力矩之间的映射关系，并利用模拟退火混合遗传算法对摩擦参数进行辨识. 为了验证所提方法的有效性，以多关节串联型机器人为研究对象，进行摩擦参数辨识实验. 实验结果表明，与传统的库伦+黏性摩擦模型相比，改进的Stribeck摩擦模型可以减少17.7%~33.6%的关节力矩计算误差，并能够进一步提高机器人动力学模型的准确性.

Identification of improved friction model for robot based on hybrid genetic algorithm

Aiming at the problem that the Coulomb and viscous friction model cannot really reflect the nonlinear characteristics of friction in the process of robot motion, an improved Stribeck friction model was used to describe the joint friction, and a method of model parameter identification based on the combination of hybrid genetic algorithm and cosine trajectory was proposed. First, different cosine trajectories were used to excite the robot joints, and the friction torque of the joints was determined using the known dynamic equations of the robot, so as to establish the mapping relationship between the robot joint velocity and the joint friction torque. Second, the simulated annealing hybrid genetic algorithm was used to identify the friction parameters. Finally, a multi-joint series robot was used as the research object, and the friction parameter identification experiments were carried out to verify the effectiveness of the proposed method. Experimental results indicate that compared with the traditional Coulomb and viscous friction model, the improved Stribeck friction model can reduce the calculation error of the joint torque by 17.7% to 33.6%, and can further improve the accuracy of the robot dynamic model.