Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The proposed recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the proposed neural network, the end-effector tracking and regulation errors asymptotically converge to zero in the presence of both input disturbance and the two constraints. Simulation examples and comparisons with an existing controller are also presented to validate the effectiveness and superiority of the proposed controller.

中文翻译：

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具有周期性输入扰动和物理约束的冗余机械手运动学控制的递归神经网络

输入干扰和物理约束是冗余机械手运动学控制中的重要问题。在本文中，我们提出了一种新颖的递归神经网络，以同时解决周期性输入扰动，关节角度约束和关节速度约束，并优化一般的二次性能指标。所提出的递归神经网络适用于调节和跟踪任务。理论分析表明，利用所提出的神经网络，在存在输入扰动和两个约束的情况下，末端执行器的跟踪和调节误差渐近收敛至零。还给出了仿真示例并与现有控制器进行了比较，以验证所提出控制器的有效性和优越性。