A velocity control of frictional servosystems using an adaptive fuzzy control

C. Hwang
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引用次数: 1

Abstract

Based on the system relative degree, the frictional servosystem is transformed into external and internal parts. By using a feedback linearizing control, the external part becomes a linear dynamic system with uncertainties. A reference model with the desired amplitude and phase properties is given to obtain an error dynamics in the presence of uncertainties. The unmatched uncertainty is also examined. To improve the system performance, an online fuzzy model is employed to model these uncertainties in a compact subset. An updating law with e-modification for the weight of fuzzy model is designed to obtain an effective learning of the uncertainties. Then, an equivalent control using the known part of frictional servosystem and the learning fuzzy model of uncertainties is established to achieve the desired result. The unmodeled dynamics caused by the error of the approximated fuzzy model and estimated weight are tackled by a switching control. In summary, the adaptive fuzzy control includes two parts: a feedback linearizing control with a reference model and an adaptive fuzzy variable structure control. The stability of the overall system is then verified by Lyapunov theory so that the uniformly ultimately bounded tracking is accomplished. Simulations are also presented to verify the usefulness of the proposed control.
基于自适应模糊控制的摩擦伺服系统速度控制
根据系统相对度,将摩擦伺服系统分解为内、外两部分。采用反馈线性化控制,使外部系统成为具有不确定性的线性动态系统。给出了具有所需振幅和相位特性的参考模型,以获得存在不确定性时的误差动力学。本文还研究了不匹配不确定性。为了提高系统的性能,采用在线模糊模型将这些不确定性建模到一个紧凑的子集中。设计了模糊模型权值的e-修正更新律,以获得对不确定性的有效学习。然后,利用摩擦伺服系统的已知部分和不确定性学习模糊模型建立等效控制,以达到预期的控制效果。通过切换控制解决了由模糊近似模型和估计权值误差引起的未建模动力学问题。综上所述,自适应模糊控制包括两个部分:带参考模型的反馈线性化控制和自适应模糊变结构控制。然后用李雅普诺夫理论验证了整个系统的稳定性,从而实现了一致的最终有界跟踪。仿真结果验证了所提控制方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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