冗余机器人控制器的设计、仿真与比较

Claudio Urrea, John Kern
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引用次数: 11

摘要

仿真工具是机器人系统设计、机器人在复杂环境中的应用以及开发新的控制策略和算法的基础。为此,本文对应用于五自由度冗余机器人的控制器进行了设计、仿真和性能比较。通过齐次变换矩阵,得到冗余度机器人的运动学逆模型。准备了六个控制器来测试机器人的动力学模型:双曲正弦-余弦;计算转矩;滑动双曲模式;用学习控制;和自适应。利用MatLab/Simulink软件开发了仿真环境,对机器人和所设计控制器的动态性能进行了分析。该仿真环境用于对冗余机械臂模型和每个控制器在空间中沿轨迹运动时进行不同的测试。仿真结果表明,与其他控制器相比,采用自适应控制器的冗余度机械臂模型遵循试验轨迹,最大误差较小,机械臂运动更加均匀。当使用计算扭矩控制器时,机器人模型测试时产生的最大关节误差和直角坐标误差均为最大值和均方根值。由于迭代次数多,控制器的变化不重要,因此通过三次迭代得到控制器的学习结果。
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Design, simulation and comparison of controllers for a redundant robot

The simulation tools are the foundation for the design of robot systems, for the application of robots in complex environments and for the development of new control strategies and algorithms. Because of this, the design, simulation and comparison of the performance of controllers applied to a redundant robot with five degrees of freedom (DOF) are presented in this paper. Through homogeneous transformation matrices the inverse kinematic model of the redundant robot is obtained. Six controllers are prepared to test the robot’s dynamic model: hyperbolic sine–cosine; computed torque; sliding hyperbolic mode; control with learning; and adaptive. A simulation environment is developed by means of the MatLab/Simulink software, which allows analyzing the dynamic performance of the robot and of the designed controllers. This simulation environment is used to carry out different tests of the redundant manipulator model together with each controller as they are made to follow a trajectory in space. The results, obtained through a simulation environment, are represented by comparative curves and RMS indices of the joint and Cartesian errors, and they show that the redundant manipulator model follows the test trajectory with less pronounced maximum errors using the adaptive controller than the other controllers, with more homogeneous motions of the manipulator. The largest joint and Cartesian errors generated when testing the robot model, both in terms of maximum and RMS values, occurred when the computed torque controller is used. The results with that controller are obtained by executing three iterations for learning, because with more iterations the variations were not important.

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