School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Neural Netw. 2022 Sep;153:64-75. doi: 10.1016/j.neunet.2022.05.021. Epub 2022 Jun 6.
Redundant manipulators could be efficient tools in industrial production as a result of their dexterity. However, existing kinematic control methods for redundant manipulators have two main disadvantages. On one hand, model uncertainties or unknown kinematic parameters may degrade the performance of existing model-based control methods subject to joint limits. On the other hand, existing model-free control methods ignore the existence of joint limits although they do not need to know kinematic models of redundant manipulators. In this paper, a quadratic programming (QP) scheme is elaborated to achieve the primary tracking control task of redundant manipulators as well as joint limits avoidance task. Besides, a gradient neurodynamics (GND) model is utilized to estimate the kinematics of redundant manipulators. Then, a primal dual neural network, which is employed to solve the QP problem, and the GND model are integrated towards developing a model-free control method constrained by joint angle and velocity limits for redundant manipulators. The visual sensory feedback is fed to the two neural networks. The efficacy of the proposed control method is demonstrated by extensive simulations and experiments, and the merits of the proposed method are also substantiated by comparisons.
冗余机械手由于其灵活性，可以成为工业生产中的有效工具。然而，现有的冗余机械手运动学控制方法有两个主要缺点。一方面，模型不确定性或未知运动学参数可能会降低关节极限下现有基于模型的控制方法的性能。另一方面，现有的无模型控制方法虽然不需要知道冗余机械手的运动学模型，但忽略了关节极限的存在。本文阐述了一种二次规划（QP）方案，以实现冗余机械手的主要跟踪控制任务和关节极限回避任务。此外，利用梯度神经动力学（GND）模型来估计冗余机械手的运动学。然后，将用于求解 QP 问题的主对偶神经网络和 GND 模型集成在一起，为冗余机械手开发一种受关节角度和速度限制的无模型控制方法。视觉传感器反馈被提供给这两个神经网络。通过广泛的仿真和实验验证了所提出控制方法的有效性，并通过比较证明了所提出方法的优点。
