考虑非线性摩擦模型的机器人动力学参数辨识

针对机器人动力学参数辨识的问题,提出了一种基于人工蜂群算法的辨识方法。考虑到关节摩擦特性,引入非线性摩擦模型,推导了机器人动力学模型的非线性形式。设计满足速度、加速度边界条件的五阶傅里叶级数作为激励轨迹来采集实验数据;利用人工蜂群算法,以蜂群为搜索单位,通过群体间的信息交流方式与优胜劣汰机制,对模型中的未知参数进行了辨识。最后,对得到的辨识模型进行了分析与验证,结果表明通过辨识得到关节预测力矩与测量力矩有较高的匹配度,所建立的非线性模型能够更好地描述机器人的动力学特性。

Dynamic Parameter Identification for Robot Manipulators with Nonlinear Friction Model

Aiming at the dynamical parameter identification for robot manipulator, the artificial bee colony algorithm for identification was proposed. Considering the friction characteristics that the friction model was unable to reappear the behavior of complex dynamic friction at low speeds, the nonlinear robot model contained the nonlinear friction model was deduced by introducing the nonlinear Daemi-Heimann model. Then, the five order Fourier series was designed as exciting trajectory to collect experimental data, which satisfied velocity and acceleration boundary conditions. With the artificial bee colony algorithm, the colony bee was employed as search unit to identify unknown parameters which included 15 minimum inertia parameters and 12 friction parameters in the model through exchanging the information and retaining the superior individual. Finally, the established model was validated and analyzed, and all the results demonstrated that the proposed identification algorithm can accurately identify the dynamical parameters, and it also had high speed convergence, strong search capability and can achieve the accurate prediction of robot driving torque. Compared with the linear dynamic model, the established nonlinear dynamical model can effectively improve the condition of sudden change about friction torque at the moment of joints commutation and can well reflect the dynamical characteristics of robot.