Robust H∞ control for uncertain delayed nonlinear systems based on standard neural network models

Authors:
Meiqin Liu
Affiliations:
Department of Systems Science and Engineering, College of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China
Venue:
Neurocomputing
Year:
2008

Citing 7
Cited 3

Neural networks for control systems: a survey

Automatica (Journal of IFAC)
Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach

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Robust H∞ control for uncertain discrete-time-delay fuzzy systems via output feedback controllers

IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks

IEEE Transactions on Neural Networks
Delayed Standard Neural Network Models for Control Systems

IEEE Transactions on Neural Networks
How delays affect neural dynamics and learning

IEEE Transactions on Neural Networks

Delay-dependent passivity criterion for discrete-time delayed standard neural network model

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Letters: Robust adaptive neural tracking control for a class of switched affine nonlinear systems

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New H∞ Controller Design for Neural Networks with Interval Time-Varing Delays in State and Observation

Neural Processing Letters

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Abstract

A neural-network-based robust output feedback H"~ control design is suggested for control of a class of nonlinear systems both with time delays and with uncertainties. In this paper, a full-order dynamic output feedback controller is designed for the delayed uncertain nonlinear system approximated by the neural network (e.g. multilayer perceptron, recurrent neural network, etc.), of which the activation functions satisfy the sector conditions. The closed-loop neural control system is transformed into a novel neural network model both with uncertainties and with time delays termed standard neural network model (SNNM). Based on the optimal robust H"~ performance analysis of the SNNM, the parameters of output feedback controllers can be obtained by solving some linear matrix inequalities (LMIs). The optimal H"~ controller ensures the robust global asymptotic stability of the closed-loop system and eliminates the effect of approximation errors, parametric uncertainties, and external disturbances. Finally, a simple example is presented to illustrate the effectiveness and the applicability of the proposed design approach.