A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems

Authors:
Xueli Wu;Jianhua Zhang;Quanmin Zhu
Affiliations:
YanShan University, Qinhuangdao 066004, China and Hebei University of Science and Technology, Shijiazhuang 050054, China;YanShan University, Qinhuangdao 066004, China;Bristol Institute of Technology, University of the West of England, Coldharbour Lane, Bristol BS161QY, UK
Venue:
Neurocomputing
Year:
2010

Citing 12
Cited 2

Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms

Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs

Automatica (Journal of IFAC)
Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm

Neurocomputing
Development of a new EDRNN procedure in control of human arm trajectories

Neurocomputing
Brief paper: Output feedback adaptive control of a class of nonlinear discrete-time systems with unknown control directions

Automatica (Journal of IFAC)
A correlation-test-based validation procedure for identified neural networks

IEEE Transactions on Neural Networks
Brief Paper: Design and performance analysis of a direct adaptive controller for nonlinear systems

Automatica (Journal of IFAC)
Brief Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form

Automatica (Journal of IFAC)
Robust adaptive control of nonlinear systems with unknown time delays

Automatica (Journal of IFAC)
Multilayer neural-net robot controller with guaranteed tracking performance

IEEE Transactions on Neural Networks
Direct adaptive NN control of a class of nonlinear systems

IEEE Transactions on Neural Networks
A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks

Pole placement controller with compensator adapted to semi-batch reactor process

ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
Letters: A generalized indirect adaptive neural networks backstepping control procedure for a class of non-affine nonlinear systems with pure-feedback prototype

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this study, a generalized procedure in identification and control of a class of time-varying-delayed nonlinear dynamic systems is developed. Under the framework, recurrent neural network is developed to accommodate the on-line identification, which the weights of the neural network are iteratively and adaptively updated through the model errors. Then indirect adaptive controller is designed based on the dichotomy principles and neural networks, which the controller output is designed as a neuron rather than an explicit input term against system states. It should be noticed that including implicit control variable in design is more challenging, but more generic in theory and practical in applications. To guarantee the correctness, rigorousness, generality of the developed results, Lyapunov stability theory is referred to prove the neural network model identification and the designed closed-loop control systems uniformly ultimately bounded stable. A number of bench mark tests are simulated to demonstrate the effectiveness and efficiency of the procedure and furthermore these could be the show cases for potential users to apply to their demanded tasks.