Identification of non-uniformly sampled Wiener systems with dead-zone non-linearities. (English) Zbl 1485.93605
Summary: In multi-rate systems, identifying non-uniformly sampled data (NUSD) models is a challenge. This study proposes an iteratively recursive least-squares identification algorithm for non-uniformly sampled Wiener systems with dead-zone non-linearities. First, an extended information vector is designed, in which both unknown parameters and inner variables exist. Then, based on the auxiliary model and iterative method, an auxiliary model-based iteratively recursive least-squares algorithm is developed to estimate the system parameters directly. Furthermore, to improve the convergence rate and disturbance rejection, a new modified forgetting factor function is presented. Compared with no or fixed forgetting factor algorithms, the proposed algorithm has a higher convergence speed and is more robust to white noise with different variances. The numerical simulation shows the effectiveness of the proposed algorithm, and it can be extended to other NUSD non-linear systems.
MSC:
| 93E12 | Identification in stochastic control theory |
| 93E24 | Least squares and related methods for stochastic control systems |
| 93C57 | Sampled-data control/observation systems |
Keywords:
parameter estimation; iterative least-squares algorithm; Wiener systems; non-uniform sampling; dead-zonesReferences:
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