Neural-Learning-Based Control for a Constrained Robotic Manipulator With Flexible Joints
Neural-Learning-Based Control for a Constrained Robotic Manipulator With Flexible Joints
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基于神经学习的柔性关节约束机器人操纵器控制
DOI：
10.1109/tnnls.2018.2803167
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发表时间：
2018-12-01
影响因子：
10.4
通讯作者：
Sun, Changyin
中科院分区：
文献类型：
--
作者：
He, Wei;Yan, Zichen;Sun, Changyin
目前，柔性关节机器人的控制技术还不够成熟。柔性关节机械臂动态系统具有许多不确定性，给控制器的设计带来了很大的挑战。本文正是基于这一问题展开研究的。为了解决这一问题，提高系统的鲁棒性，提出了全状态反馈神经网络控制。此外，还实现了RMFJ的输出约束，提高了机器人的安全性。通过Lyapunov稳定性分析，证明了通过选择适当的控制增益，所提出的控制器不仅能保证柔性关节机械臂系统的稳定性，而且能保证系统状态变量的有界性。然后，进行了必要的仿真实验，验证了控制器的合理性。最后，在Baxter上进行了一系列控制实验。通过与比例导数控制和基于刚性机械臂模型的神经网络控制的比较，验证了基于柔性关节机械臂模型的神经网络控制的可行性和有效性。
Nowadays, the control technology of the robotic manipulator with flexible joints (RMFJ) is not mature enough. The flexible-joint manipulator dynamic system possesses many uncertainties, which brings a great challenge to the controller design. This paper is motivated by this problem. In order to deal with this and enhance the system robustness, the full-state feedback neural network (NN) control is proposed. Moreover, output constraints of the RMFJ are achieved, which improve the security of the robot. Through the Lyapunov stability analysis, we identify that the proposed controller can guarantee not only the stability of flexible-joint manipulator system but also the boundedness of system state variables by choosing appropriate control gains. Then, we make some necessary simulation experiments to verify the rationality of our controllers. Finally, a series of control experiments are conducted on the Baxter. By comparing with the proportional-derivative control and the NN control with the rigid manipulator model, the feasibility and the effectiveness of NN control based on flexible-joint manipulator model are verified.