title: An optimal tracking neuro-controller for nonlinear dynamic systems.
abstract: 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.
authors.affiliation: Dept. of Electr. Eng., Seoul Nat. Univ.
authors.forename: Y M
authors.initials: YM
authors.lastname: Park
authors.forename: M S
authors.initials: MS
authors.lastname: Choi
authors.forename: K Y
authors.initials: KY
authors.lastname: Lee