In this article, a simultaneous learning and control scheme built on the joint velocity level with physical constraints on the decision variable and its derivative, i.e., joint angle, joint velocity, and joint acceleration constraints, is proposed for the redundant manipulator control. The scheme works when the structure parameters involved in the forward kinematics are unknown or implicit. The learning and control parts are incorporated simultaneously in the scheme, which is finally formulated as a quadratic programming problem solved by a devised recurrent neural network (RNN). The convergences of learning and control abilities of the RNN are proved theoretically. Simulations and physical experiments on a 7-degrees of freedom (DOFs) redundant manipulator show that, aided with the proposed scheme and the related RNN solver, a redundant manipulator with unknown structure parameters can perform a given inverse kinematics task with high accuracy while satisfying physical constraints on the decision variable and its derivative.

中文翻译：

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决策变量及其导数物理约束的冗余机械手同步学习与控制方案

在本文中，提出了一种建立在关节速度水平上的同时学习和控制方案，该方案对决策变量及其导数进行物理约束，即关节角度、关节速度和关节加速度约束，用于冗余机械手控制。当正向运动学中涉及的结构参数未知或隐含时，该方案有效。学习和控制部分同时包含在该方案中，最终被表述为通过设计的递归神经网络 (RNN) 解决的二次规划问题。理论上证明了RNN的学习和控制能力的收敛性。对 7 自由度 (DOF) 冗余机械手的模拟和物理实验表明，在所提出的方案和相关 RNN 求解器的帮助下，