Study on the control of the position / force of the manipulator with the neural network model

海洋机器人的机械手要求其具有适应各种场合和精细运动的能力。因此，机械手的控制系统需要一些先进的控制方法，这就要求对末端执行器的位置和作用力进行控制。近年来，神经网络的应用越来越受到人们的关注。因此，本文利用神经网络模型研究了末端执行器位置和作用力的控制方法对末端执行器的影响。(1)采用基于机械手动力学逆系统的自适应神经滤波前馈控制器作为内模，学习过程采用反馈-误差-学习策略。与传统的反馈控制系统相比，该控制系统能够实现精确的操作，并能适应对象特性的变化。(2)通过改变学习的时间常数，学习速度比通常的方法更快。(3)利用现有方法，提出了一种实用的机械手位置和力的混合控制方法。根据这些结果，为海洋机器人机械手的操作技术奠定了基础。我们正在规划控制方法的研究，以降低操作的能量，它考虑了海洋机器人的特殊环境。

The manipulator for the ocean robot is required the ability to adapt its manipulation to various occasion and delicate movement. So some advanced control method are necessary to the control system of the manipulator, and it will be demanded the control of the position of the end effector and the force affected it. Recently, it is paid attention to the application of the neural network. Therefore, in this report, it is represented that the control method of the position of the end effector and the force affected it using the neural network model.(1) The feed forward controller which uses an adaptive neuro-filter employing the inverse system of dynamics of the manipulator is adopted as an internal model, and the feedback-error-learning scheme is used for the learning process. This control system enables precise operations compared to the conventional feedback control systems, and is adaptable to the change of the character of the plant.(2) By turning the time constants of the learning, the learning speed which is one of the difficult problem of the neural network is faster than the usual method.(3) By using present method, it is developed a practical hybrid control method of the position and the force of the manipulator.From these result, it would be established the foundation of the manipulation technique to the manipulator for the ocean robot.We are planning the study of the control method to reduce the energy of the manipulation, which considers the particular environment of the ocean robot.