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Robust performance enhancement using disturbance observers for hysteresis compensation based on generalized Prandtl-Ishlinskii model

机译:使用基于广义Prandtl-Ishlinskii模型的扰动观测器进行磁滞补偿的鲁棒性能增强

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This paper presents an approach employing disturbance observers (DOBs) to enhance the performance of inverse-based hysteresis compensation based on the generalized Prandtl-Ishlinskii model in feedback control reference-tracking applications. It is first shown that the error resulting from inexact hysteresis cancelation is an L∞-bounded signal. Hence, a DOB is designed to cancel its effect and improve the closed loop robust tracking performance in the presence of plant uncertainty. The design of the DOB makes use of an equivalent internal model-based estimation of exogenous disturbances, where the internal model dynamics is designed to have at least one of its eigen-values at the origin. The synthesis is then formulated as an H∞ weighted-sensitivity optimization for static output feedback (SOF) gain of a Luenberger observer. A linearization heuristic is then implemented to solve the bilinear-matrix-inequality (BMI) constrained semi-definite program (SDP) for a (sub)optimal static gain. Simulation results indicate that tracking performance is indeed improved using the combined inversion-based compensation and the DOB.
机译:本文提出了一种在反馈控制参考跟踪应用中采用基于广义Prandtl-Ishlinskii模型的扰动观测器(DOB)来提高基于逆的磁滞补偿性能的方法。首先表明,由不精确的磁滞消除引起的误差是一个L∞有界信号。因此,DOB旨在消除其影响并在存在工厂不确定性的情况下提高闭环鲁棒跟踪性能。 DOB的设计利用了等效的基于内部模型的外源性干扰估计,其中内部模型动力学被设计为至少具有其特征值之一。然后将合成公式化为针对Luenberger观测器的静态输出反馈(SOF)增益的H∞加权灵敏度优化。然后实施线性化启发式算法,以求解(次)最优静态增益的双线性矩阵不等式(BMI)约束半定程序(SDP)。仿真结果表明,结合使用基于反演的补偿和DOB,跟踪性能确实得到了改善。

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