An Optimal Tracking Neuro-Controller for Nonlinear Dynamic Systems  

Oleh:

Park, Young-Moon ; Choi, Myeon-Song ; Lee, K.Y.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 5 (1996), page 1099-1110.
Topik: tracking systems; optimal tracking; neuro - controller; non linear dynamic systems

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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