In order to effectively decrease the joint-angular drifts and end-effector position accumulation errors, a novel adaptive fuzzy recurrent neural network (AFRNN) is proposed and exploited to solve the nonrepetitive motion problem of redundant robot manipulators in this paper. First, a quadratic programming (QP)-based repetitive motion scheme is designed according to the kinematics constraint of redundant robot manipulators. Second, the QP-based repetitive motion scheme is converted to a matrix equation according to the Lagrangian multiplier method. Third, inspired by the neural-dynamic and fuzzy control theory, the AFRNN model is designed, which can effectively solve the matrix equation as well as the original nonrepetitive motion problem of redundant robot manipulators. Computer simulation results verify the effectiveness, high accuracy, and robustness to resist external disturbance of the proposed AFRNN scheme.

中文翻译：

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解决冗余机器人机械臂非重复运动问题的自适应模糊循环神经网络


为了有效减少关节角漂移和末端执行器位置累积误差，本文提出并利用一种新型自适应模糊递归神经网络（AFRNN）来解决冗余机器人机械臂的非重复运动问题。首先，根据冗余机器人机械臂的运动学约束，设计了基于二次规划（QP）的重复运动方案。其次，根据拉格朗日乘子法将基于QP的重复运动方案转换为矩阵方程。第三，受神经动力学和模糊控制理论的启发，设计了AFRNN模型，可以有效地解决矩阵方程以及冗余机器人机械臂的原始非重复运动问题。计算机仿真结果验证了所提出的 AFRNN 方案的有效性、高精度和抗外部干扰的鲁棒性。