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Iterative learning parametrization for pointwise pole assignment in linear dynamical systems

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Abstract

By means of iterative learning parametrization for state/output feedback gains in linear time invariant systems, pointwise pole assignment (PPA) is re-formulated and addressed in a complex-domain fashion, whereas implementation issues are also examined. Technical features include: (i) no assumptions other than controllability and observability are needed; (ii) the iterative learning parametrization algorithms are numerically tractable and robust against initial values and matrix uncertainties; (iii) the suggested algorithms are significant for achieving PPA-related control strategies, where data-modeling and data-driving techniques are employed. Numerical examples are included to illustrate the main results.

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Acknowledgements

The study is supported by the National Natural Science Foundation of China under Grant No. 61573001.

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Authors and Affiliations

  1. School of Energy and Electrical Engineering, Hohai University, Nanjing, 211100, China

    Jun Zhou & Tiantian Yan

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  1. Jun Zhou
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  2. Tiantian Yan
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Correspondence to Tiantian Yan.

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Zhou, J., Yan, T. Iterative learning parametrization for pointwise pole assignment in linear dynamical systems. Int. J. Dynam. Control 10, 194–202 (2022). https://doi.org/10.1007/s40435-021-00800-9

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  • DOI: https://doi.org/10.1007/s40435-021-00800-9

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