Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time
Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time
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DOI：
10.3390/robotics10040115
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发表时间：
2021-12-01
期刊：
影响因子：
3.7
通讯作者：
Sun, Jian-Qiao
中科院分区：
文献类型：
--
作者：
Gholami, Akram;Homayouni, Taymaz;Sun, Jian-Qiao
提出了一种基于神经网络的Delta机器人逆运动学控制器。所开发的控制方案是纯粹的数据驱动，不需要三角洲机器人运动学的先验知识。而且，它可以适应机器人运动学的变化。为了开发控制器，三角形机器人的运动学模型估计使用神经网络。然后，将训练好的神经网络配置为系统中的控制器。神经网络的参数更新，而机器人遵循的路径，以自适应补偿模型的不确定性和外部干扰的控制系统。本文的主要贡献之一是表明，更新神经网络的参数提供了一个较小的跟踪误差在逆运动控制的三角形机器人与关节间隙的考虑。不同的仿真和实验进行了验证所提出的控制器。结果表明，在存在外部干扰的情况下，轨迹跟踪误差有界，减小了关节侧隙对轨迹跟踪的负面影响。该方法为Delta机器人的逆运动学控制提供了一种新的途径。
This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.