A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems

For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key is using the filtering technique and the multi-innovation identification theory. The proposed algorithms can identify the parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm can give more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithms work well.