Training backpropagation and CMAC neural networks for control of a SCARA robot
Training backpropagation and CMAC neural networks for control of a SCARA robot
复制标题
训练反向传播和 CMAC 神经网络以控制 SCARA 机器人
DOI：
10.1016/0952-1976(93)90026-t
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发表时间：
1993
影响因子：
8
通讯作者：
D. Garg
中科院分区：
文献类型：
--
作者：
S. Ananthraman;D. Garg
机械手的动态控制是通过计算和施加机械手跟踪期望轨迹所需的驱动力矩来实现的。大量的工作已经报道在文献中关于经典的基于模型的自适应控制技术的应用上述问题。然而，许多可用的计划遭受的事实，他们需要一个准确的模型的机器人动力学，包括非线性，这可能是很难获得事先为了解决问题的自适应控制在未知的环境中，它是可能的，利用人工神经网络来学习系统的特性，而不是必须预先指定一个明确的系统模型。在本文中，两个人工神经网络为基础的战略实现了准确的轨迹跟踪的SCARA型IBM 7540机器人。将基于反向传播的神经控制器的性能与基于类似于Albus的小脑模型关节控制器（CMAC）1的方案的神经控制器的性能进行比较[Albus J.S.美国机械工程师学会动态系统测量杂志。Control，pp. 220-227（1975）]。
The dynamic control of a robotic manipulator is accomplished by the computation and application of actuating torques required for the manipulator to follow desired trajectories. A considerable amount of work has been reported in the literature concerning the application of classical model-based and adaptive control techniques to the above problem. However, many of the available schemes suffer from the fact that they require an accurate model of the robot dynamics, including nonlinearities, which may be difficult to obtain beforehand.In order to address the problem of adaptive control in unknown environments, it is possible to utilize artificial neural networks to learn the characteristics of the system rather than having to prespecify an explicit system model. In this paper, two artificial-neural-network-based strategies are implemented for the accurate trajectory tracking by a SCARA-type IBM 7540 robot. The performance of a backpropagation-based neural controller is compared with that of one based on a scheme similar to Albus' Cerebellar Model Articulation Controller (CMAC)1[Albus J. S. Trans. ASME J. Dynamic Syst. Measur. Control, pp. 220–227 (1975)].