Linear Recurrent Neural Network for open- and closed-loop consistent identification of LPV models

In this paper a Linear Recurrent Neural Network (LRNN) approach is used to consistently identify input-output Linear Parameter Varying (LPV) systems with additive output noise in input-output representation. Moreover, an indirect identification approach based on structured LRNN is proposed for consistent identification of input-output LPV models in closed-loop. The structured LRNN is trained to identify the closed-loop system from the reference to the output signal, where the controller parameters are presented as fixed weights and the parameters of the LPV model as unknown weights. The open-loop model can then be easily extracted from the identified closed-loop model. The proposed approach is illustrated with simulation examples, and a comparison with an existing approach is given.