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Reliable online adaptation of models with variable time delays

机译:可靠的在线自适应可变时滞模型

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摘要

A new technique is presented for online adaptation of model parameters and time delays. The general case of continuous-time nonlinear models is considered, and the time delays are estimated to a fraction of the sampling period. The technique has the advantage that it requires user specification of only one parameter that determines the covered range of variability in the model parameters and time delays, whereas conventional techniques require a different user-specified parameter value corresponding to each particular variability. Prior knowledge of negligible or very high variability of certain model parameters or time delays can readily be incorporated into the proposed strategy. Performance of the technique is demonstrated on simulation runs for a single-input single-output system, with two parameters and one time delay, and comparisons are made with a conventional limited-memory estimator.
机译:提出了一种在线调整模型参数和时间延迟的新技术。考虑了连续时间非线性模型的一般情况,并且将时延估计为采样周期的一小部分。该技术的优点在于,它仅需要用户指定一个参数,该参数确定了模型参数和时间延迟的变化范围,而常规技术则需要与每个特定变化性相对应的不同用户指定参数值。可以将某些模型参数的可忽略性或非常高的可变性或时间延迟的现有知识轻松纳入建议的策略中。在具有两个参数和一个时间延迟的单输入单输出系统的仿真运行中证明了该技术的性能,并与常规的有限内存估计器进行了比较。

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