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Robust estimation without positive real condition

机译:可靠的估计,没有真实的真实条件

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摘要

The strictly positive real (SPR) condition on the noise model is necessary for a discrete-time linear stochastic control system with unmodeled dynamics, even so for a time-invariant ARMAX system, in the past robust analysis of parameter estimation. However, this condition is hardly satisfied for a high-order and/or multidimensional system with correlated noise. The main work in this paper is to show that for robust parameter estimation and adaptive tracking, as well as closed-loop system stabilization, the SPR condition is replaced by a stable matrix polynomial. The main method is to design a “two-step” recursive least squares algorithm with or without a weighted factor and with a fixed lag regressive vector and to define an adaptive control with bounded external excitation and with randomly varying truncation
机译:在过去的参数估计分析中,噪声模型上的严格正实(SPR)条件对于具有未建模动力学的离散时间线性随机控制系统是必需的,即使对于时不变的ARMAX系统也是如此。但是,对于具有相关噪声的高阶和/或多维系统,很难满足此条件。本文的主要工作是表明,对于鲁棒的参数估计和自适应跟踪以及闭环系统稳定,SPR条件由稳定的矩阵多项式代替。主要方法是设计具有或不具有加权因子且具有固定滞后回归向量的“两步”递归最小二乘算法,并定义具有有界外部激励和随机变化截断的自适应控制

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