Exponentially weighted iterative solutions for worst-case parameter estimation

This paper proposes an iterative scheme for worst-case parameter estimation in the presence of bounded model uncertainties. The algortihm distinguished itself from other estmation schemes, such as errors-in-variables and Hoo methors, in that it leads to less conservative designs since it explicitly incorporates an a-priori bound on the size of the uncertainties. It also employs an exponential weighting scheme where data in the remote past are given less weight than the most recent measurements. This feature is especially useful in tracking problms where recent observations carry more information about the current value of the unknown parameter. Simulation results are included to demonstrate the performance of the recursive scheme.


Published in:
Proceedings of the 5th IEEE Mediterranean Conference on Control and Systems
Presented at:
5th IEEE Mediterranean Conference on Control and Systems, Cyprus, July, 1997
Year:
1997
Laboratories:




 Record created 2017-12-19, last modified 2018-03-17


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