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Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks?

机译:基于摩擦观测器和递归模糊神经网络的不确定伺服系统鲁棒滑模控制?

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摘要

A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme.
机译:使用摩擦参数观测器和基于滑模控制的递归模糊神经网络,开发了一种鲁棒的定位控制方案。作为动态摩擦模型,LuGre模型用于处理摩擦补偿,因为已知它可以充分捕获非线性动态摩擦的特性。开发的摩擦参数观测器结构简单,并且可以很好地估算LuGre摩擦模型的摩擦参数。另外,利用递归模糊神经网络技术开发了系统不确定度的近似方法,以提高定位精度。一些仿真和实验对所提出的鲁棒控制方案的性能进行了验证。

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