An Approximate Internal Model-Based Neural Control for Unknown Nonlinear Discrete Processes  

Oleh:

Li, Han-Xiong ; Deng, Hua

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 659-670.
Topik: DISCRETE SIGNAL; internal; model - based; neural control; non linear; discrete process

Ketersediaan

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

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Isi artikel

An approximate internal model - based neural control (AIMNC) strategy is proposed for unknown non affine nonlinear discrete processes under disturbed environment. The proposed control strategy has some clear advantages in respect to existing neural internal model control methods. It can be used for open - loop unstable nonlinear processes or a class of systems with unstable zero dynamics. Based on a novel input - output approximation, the proposed neural control law can be derived directly and implemented straightforward for an unknown process. Only one neural network needs to be trained and control algorithm can be directly obtained from model identification without further training. The stability and robustness of a closed - loop system can be derived analytically. Extensive simulations demonstrate the superior performance of the proposed AIMNC strategy.

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