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Multi-model recursive identification for nonlinear systems with non-uniformly sampling

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Abstract

A recursive least squares based on Multi-model is proposed for non-uniformly sampled-data nonlinear (NUSDN) systems. The corresponding state space model of an NUSDN system is derived using lifting technique. Taking advantage of the Fuzzy c-Mean Clustering algorithm, NUSDN is divided into several local models. The basic idea is that the NUSDN system is viewed as a model switching system under a given rule. Once the local models are identified, the global model is determined. A pH neutralization process validate the performance of the proposed algorithm.

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Acknowledgements

The authors would like to thank the financial support provided by National Natural Science Foundation under Grant 61273142, Foundation for Six Talents by Jiangsu Province (2012-DZXX-045), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu support of science and technology projects under Grant BY2016030-16 Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No.1601138B).

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Authors and Affiliations

  1. School of Automotive and Traffic Engineering, Jiangsu University of Technology, Changzhou, 213001, China

    Ranran Liu

  2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Ranran Liu, Tianhong Pan & Zhengming Li

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  1. Ranran Liu
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  2. Tianhong Pan
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  3. Zhengming Li
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Correspondence to Tianhong Pan.

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Liu, R., Pan, T. & Li, Z. Multi-model recursive identification for nonlinear systems with non-uniformly sampling. Cluster Comput 20, 25–32 (2017). https://doi.org/10.1007/s10586-016-0688-0

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