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

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. (C) 2009 Elsevier B.V. All rights reserved.