Adaptive Control Using Neural Networks and Approximate Models  

Oleh:

Mukhopadhyay, S. ; Narendra, K. S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 3 (1997), page 475-485.
Topik: NEURAL NETWORKS; adaptive; neural networks; approximate models

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The NARMA model is an exact representation of the input - output behaviour of finite - dimensional non linear discrete - time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its non linear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input - output models warrant a detailed study in their own right.

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