A multimodel recurrent neural network for systems identification and control

A parametric recurrent neural network (RNN) model and an improved dynamic backpropagation method of its learning are applied for real-time identification and state estimation of nonlinear plants. This RNN architecture has been expanded in a multimodel sense to identification of complex nonlinear plants. The obtained parameters of the RNN model are used for an adaptive control system design. The paper suggests performing a trajectory tracking state-space control for both cases. The applicability of the proposed adaptive control schemes is confirmed by simulation results.