Neural network learning control of robot manipulators using gradually increasing task difficulty
Neural network learning control of robot manipulators using gradually increasing task difficulty
复制标题
使用逐渐增加的任务难度的机器人机械手的神经网络学习控制
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
T. Sanger
中科院分区：
文献类型：
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作者：
T. Sanger
轨迹可拓学习是一种增量式的方法，用于训练人工神经网络来逼近机器人操作器的逆动力学。期望轨迹附近的训练数据通过从逆动力学的易解性区域朝向期望行为缓慢地改变轨迹的参数来获得。该参数可以是平均速度、路径形状、反馈增益或任何其他可控变量。随着学习的进行，用于参数的每个值的局部逆动力学的近似解被用于引导用于参数的下一个值的学习。给出了该算法的两种变体的收敛条件。应用程序的例子示出了一个真实的2关节直接驱动机器人手臂和一个模拟的3关节冗余臂，都使用模拟平衡点控制。>
Trajectory extension learning is an incremental method for training an artificial neural network to approximate the inverse dynamics of a robot manipulator. Training data near a desired trajectory is obtained by slowly varying a parameter of the trajectory from a region of easy solvability of the inverse dynamics toward the desired behavior. The parameter can be average speed, path shape, feedback gain, or any other controllable variable. As learning proceeds, an approximate solution to the local inverse dynamics for each value of the parameter is used to guide learning for the next value of the parameter. Convergence conditions are given for two variations on the algorithm. Examples are shown of application to a real 2-joint direct drive robot arm and a simulated 3-joint redundant arm, both using simulated equilibrium point control. >
影响因子：
56.9
作者：
BIZZI, E;MUSSAIVALDI, FA;GISZTER, S
通讯作者：
GISZTER, S