A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise

In this paper, an adaptive identification scheme is proposed for nonlinear multi-input multi-output systems with colored noise based on a novel parameter update law. With the help of the hierarchical principle, the identification model is decomposed into three sub-models in which the computational burden is reduced. For each sub-model, the identification algorithm is proposed to estimate the sub-model parameters. In the process of the identification algorithm design, considering the system information corrupted by the noise, an adaptive filter gain is exploited to extract helpful identification data, in which a filter is designed using the system data instead of the independent design. Based on several auxiliary filtered variables, the estimation error data are obtained, and a new parameter adaptive law with a variable learning gain is proposed according to the estimation error data. Compared with the classic parameter update law, the parameter estimation update is derived based on the estimation error information instead of other error information, such as prediction error information. Under the persistent excitation condition, all the estimated parameters converge to the true parameters. an example is used and two experiments are conducted to test the outstanding identification performance of the proposed algorithm in terms of convergence rate and identification accuracy.