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Application of symplectic group in a novel branch of soft computing for controlling of electro-mechanical devices

机译:辛基组在控制电力装置的软计算新分支中的应用

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An application of a special approach aiming at the development of a new branch of Soft Computing (SC) for the adaptive control of approximately and partially known electromechanical systems is reported in this paper. Like "traditional" SC it uses "uniform structures" for modeling, but these structures are obtained from the Symplectic Group (SG). This approach can considerably reduce the number of free parameters in the model in comparison e.g. with that of the neural networks or ample sets of fuzzy rules. It also replaces the process of parameter tuning with simple, lucid, and explicit algebraic operations of limited steps. While in the previous approaches SG was utilized as an inner symmetry of classical mechanical systems, in the present one it is used in a formal way not utilizing mechanical symmetries. Furthermore, SG is not the internal symmetry of electrical, that is of electro-mechanical systems. In the present paper the new formal approach is demonstrated in the adaptive control of a 3 mechanical DOF SCARA-type robot arm actuated by DC motors together forming a 6 DOF electromechanical system. It is concluded on the basis of simulations that the control designed for the 3 DOF mechanical degrees of freedom can efficiently compensate for the behavior of the electrical degrees of freedom npot modeled by the controller.
机译:本文报道了一种针对大致和部分已知的机电系统的自适应控制开发的特殊方法的应用,用于大致和部分已知的机电系统的自适应控制。与“传统”SC一样,它使用“统一结构”来建模,但这些结构是从辛族(SG)获得的。这种方法可以大大减少模型中的自由参数的数量,例如,例如,具有神经网络或充足的模糊规则。它还替换了有限步骤的简单,清晰,显式代数操作的参数调整过程。虽然在先前的方法中,SG被用作经典机械系统的内部对称性,但在本发明中,它以正式的方式使用,不利用机械对称。此外,SG不是电气的内部对称性,即机电系统。在本文中,新的正式方法是在由DC电机致动的3个机械DOF SCARA型机器人手臂的自适应控制中进行了说明,该系统一起形成6 DOF机电系统。它基于模拟的基础上,设计为3 DOF机械自由度的控制可以有效地补偿控制器模型的自由度的电气程度的行为。

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