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Highly computationally efficient parameter estimation algorithms for a class of nonlinear multivariable systems by utilizing the state estimates

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Abstract

This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the considered system is derived. Second, by cutting down the redundant parameter estimates and extracting the unique parameter estimates from the parameter estimation vector in the least-squares identification method, we present an over-parameterization-based partially coupled average recursive extended least-squares parameter estimation algorithm to estimate the parameters. As for the unknown states in the parameter estimation algorithm, a new state estimator is designed to generate the state estimates. Third, in order to improve the computational efficiency of the parameter estimation algorithm, an over-parameterization-based multi-stage partially coupled average recursive extended least-squares algorithm is proposed. Finally, the computational efficiency analysis and the simulation examples are given to verify the effectiveness of the proposed algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61873111, 62273167).

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  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

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Cui, T., Ding, F. Highly computationally efficient parameter estimation algorithms for a class of nonlinear multivariable systems by utilizing the state estimates. Nonlinear Dyn 111, 8477–8496 (2023). https://doi.org/10.1007/s11071-023-08259-3

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