Stable Adaptive Neurocontrol for Nonlinear Discrete-time Systems  

Oleh:

Zhu, Quanmin ; GUo, Lingzhong

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 3 (May 2004), page 653-662.
Topik: non linear; stable; adaptive neurocontrol; non linear; discrete - time systems

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete - time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex non linear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network weight adaptation law is derived from Lyapunov stability analysis and the connection between convergence of the network weight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed - loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach.

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