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[ $H_{\infty}$ ] Multi-Step Prediction for Linear Discrete-Time Systems: A Distributed Algorithm  

Wang, Hao-Qian (Department of Automation, Tsinghua University)
Zhang, Huan-Shui (School of Control Science and Engineering, Shandong University)
Hu, Hong (Shenzhen Graduate School of Harin Institute of Technology)
Publication Information
International Journal of Control, Automation, and Systems / v.6, no.1, 2008 , pp. 135-141 More about this Journal
Abstract
A new approach to $H_{\infty}$ multi-step prediction is developed by applying the innovation analysis theory. Although the predictor is derived by resorting to state augmentation, nevertheless, it is completely different from the previous works with state augmentation. The augmented state here is considered just as a theoretical mathematic tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. By using the reorganized innovation analysis, calculation of the estimator does not require any augmentation. A numerical example demonstrates the effect in reducing computing burden.
Keywords
Distributed algorithm; $H_{\infty}$ estimation; innovation; Krein space;
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