Neural-Network Predictive Control for Nonlinear Dynamic Systems With Time-Delay  

Oleh:

Lewis, F. L. ; Huang, Jin-Quan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 2 (2003), page 377-389.
Topik: time; developmental delay; neural - network; predictive control; non linear dynamic systems; time - delay

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A new recurrent neural - network predictive feedback control structure for a class of uncertain nonlinear dynamic time - delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on - line weight tuning algorithm is employed to approximate the dynamics of the time - delay - free non linear plant. No linearity in the unknown parameters is required. No preliminary off - line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance ; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown non linear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.

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