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Auxiliary model-based least-squares identification methods for Hammerstein output-error systems |
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| Authors: | Feng Ding Yang Shi Tongwen Chen |
| Affiliation: | aControl Science and Engineering Research Center, Southern Yangtze University, Wuxi 214122, P.R. China;bDepartment of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada S7N 5A9;cDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada T6G 2V4 |
| Abstract: | The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the information vector in the identification model contains unknown variables—the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm indicates that the parameter estimation error consistently converges to zero under a generalized persistent excitation condition. The simulation results show the effectiveness of the proposed algorithms. |
| Keywords: | Recursive identification Parameter estimation Least squares Multi-innovation identification Hierarchical identification Auxiliary model Convergence properties Stochastic gradient Hammerstein models Wiener models Martingale convergence theorem |
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