An optimal tracking neuro-controller for nonlinear dynamic systems

Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function, The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC), The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant, The FFNC is designed using a novel inverse mapping concept by using a neuro-identifier, A generalized backpropagation-through-time (GBTT) algorithm is developed to minimize the general quadratic cost function for the FBNC training, The proposed methodology is useful as an off-line control method where the plant is first identified and then a controller is designed for it, A case study for a typical plant with nonlinear dynamics shows good performance of the proposed OTNC.