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On some limitations of adaptive feedback measurement algorithm

机译:关于自适应反馈测量算法的一些局限性

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摘要

The brilliant idea of Adaptive Feedback Control Systems (AFCS) makes possible creation of highly efficient adaptive systems for estimation, identification and filtering of signals and physical processes. The research problem considered in this paper is: how performance of AFCS changes if some of the assumptions used to formulate iterative estimation algorithm are not fulfilled exactly. To limit the scope of research a particular implementation of the AFCS concept was considered, i.e. an adaptive feedback measurement system (AFMS). The iterative measurement algorithm used was derived under some idealized conditions, notably with perfect knowledge of the system model and Gaussian communication channels. The selected non-idealities of interest are non-zero mean value of noise processes and non-ideal calibration of transmission gain in the forward channel - because they are related to intrinsic non-idealities of analog building blocks, used for the AFMS implementation. The presented original analysis of the iterative measurement algorithm provides quantitative information on speed of convergence and limit behavior. The analysis should be useful for AFCS implementors in the measurement area - since the results are presented in terms of accuracy and precision of iterative measurement process.
机译:自适应反馈控制系统(AFCS)的绝妙想法使创建高效的自适应系统成为可能,从而可以对信号和物理过程进行估计,识别和过滤。本文考虑的研究问题是:如果不能完全满足用于制定迭代估计算法的某些假设,AFCS的性能将如何变化。为了限制研究范围,考虑了AFCS概念的特定实施方式,即自适应反馈测量系统(AFMS)。所使用的迭代测量算法是在某些理想条件下得出的,尤其是在对系统模型和高斯通信通道有全面了解的情况下。所选的非理想非理想值是噪声过程的非零平均值以及前向信道中传输增益的非理想校准-因为它们与用于AFMS实现的模拟构件的固有非理想性有关。提出的对迭代测量算法的原始分析提供了关于收敛速度和极限行为的定量信息。该分析对于测量领域的AFCS实施者应该是有用的-因为结果是以迭代测量过程的准确性和精确性表示的。

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