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Application of adaptive neural network based interval type-2 fuzzy logic control on a nonlinear system

机译:基于自适应神经网络的区间2型模糊逻辑控制在非线性系统中的应用

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In this study, four adaptive neural network based fuzzy logic controllers (ANNFL) are designed and used as two controllers in terms of interval type-2 fuzzy logic control. The new controllers are called as adaptive neural network based interval type-2 fuzzy logic controller (ANNIT2FL) and applied to a rigid-flexible robot manipulator. Initially dynamic model of the manipulator is obtained by using Lagrange equations and assumed modes method. ANNFL controllers are used for tracking and vibration control of system. The training and testing data of ANNFLs are obtained from the conventional PD control of the manipulator system. The performances of four ANFLCs are tested for different type and different number of membership functions and combined to create two ANNIT2FL controllers. Finally simulation results are obtained according to rotation and vibration control performances of ANNIT2FL controllers. Results demonstrate the remarkable performance of the proposed control technique.
机译:在这项研究中,设计了四个基于自适应神经网络的模糊逻辑控制器(ANNFL),并将其用作区间2型模糊逻辑控制的两个控制器。新的控制器称为基于自适应神经网络的区间2型模糊逻辑控制器(ANNIT2FL),并应用于刚性-柔性机器人操纵器。通过使用拉格朗日方程和假定模式方法获得了机械手的初始动态模型。 ANNFL控制器用于系统的跟踪和振动控制。 ANNFL的训练和测试数据是从机械手系统的常规PD控制获得的。测试了四个ANFLC的性能以用于不同类型和不同数量的隶属函数,并组合创建了两个ANNIT2FL控制器。最后根据ANNIT2FL控制器的旋转和振动控制性能获得了仿真结果。结果证明了所提出的控制技术的卓越性能。

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