Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real-time observed data, a cost function with dynamical data is constructed to capture on-line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.

Automated summary

This article proposes an auxiliary model stochastic gradient identification approach based on gradient optimization and develops an auxiliary model multiinnovation stochastic gradient estimation method to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation results demonstrate the effectiveness of the proposed auxiliary model identification method for nonlinear sandwich systems.