Direct Adaptive Controller for Nonaffine Nonlinear Systems Using Self-Structuring Neural Networks  

Oleh:

Park, Jang-Hyun ; Huh, Sung-Hoe ; Kim, Seong-Hwan ; Seo, Sam-Jun ; Park, Gwi-Tae

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 2 (Mar. 2005), page 414-422.
Topik: neural network; direct adaptive; controller; non affine; non linear systems; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A direct adaptive state - feedback controller is proposed for highly non linear systems. We consider uncertain or ill - defined non affine non linear systems and employ a neural network (NN) with flexible structure, i. e., an online variation of the number of neurons. The NN approximates and adaptively cancels an unknown plant non linearity. A control law and adaptive laws for the weights in the hidden layer and output layer of the NN are established so that the whole closed - loop system is stable in the sense of Lyapunov. Moreover, the tracking error is guaranteed to be uniformly asymptotically stable (UAS) rather than uniformly ultimately bounded (UUB) with the aid of an additional robustifying control term. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. The efficiency of the proposed scheme is shown through the simulation of a simple non affine non linear system.

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