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Neural networks for force control of an assembly robot

机译:用于部件机器人的力量控制的神经网络

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The performing of robotized assembly tasks implies hard constraints like the knowledge of robot dynamics and robot-environment interaction. So classical form control laws lead to low performances. The aim of this paper is to show the ability of neural techniques for learning control under real world conditions of unknown non-linear systems. In our control strategy neural networks are used for the identification and control of an assembly parallel robot performing a peg-in-hole insertion. Such a system is strongly non linear. In this paper, we propose a new approach of hybrid force position control based on feedforward neural networks. The proposed structure does not need any mathematical modelling of system dynamics. Experimental results show that identification and control schemes suggested in this paper are practically feasible.
机译:对机器人化的装配任务的执行意味着像机器人动力学和机器人环境交互的知识一样的硬约束。所以古典形式控制法律导致低性能。本文的目的是展示神经技术在未知的非线性系统的真实世界条件下学习控制的能力。在我们的控制策略中,神经网络用于识别和控制执行PEG孔插入的组件并联机器人。这种系统强烈非线性。本文提出了一种基于前馈神经网络的混合力位置控制的新方法。所提出的结构不需要任何系统动态的数学建模。实验结果表明,本文建议的识别和控制方案实际上是可行的。

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