Least squares algorithm for an input nonlinear system with a dynamic subspace state space model. (English) Zbl 1281.93050

Summary: For a Hammerstein input nonlinear system with a subspace state space linear element, this paper transforms the system into a bilinear identification model by using the property of the shift operator to the state space model and presents a recursive and an iterative least squares algorithms to generate parameter estimates and state estimates by using the hierarchical identification principle and by replacing the unknown state variables with their estimates. The proposed approaches are computationally more efficient than the over-parameterization model based least squares method.


93C10 Nonlinear systems in control theory
93E24 Least squares and related methods for stochastic control systems
93E12 Identification in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93C25 Control/observation systems in abstract spaces
Full Text: DOI


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