Neural networks (NNs) have proven to be effective tools for identification, estimation and control of complex uncertain nonlinear systems. As a natural extension of feedforward NNs with the capability to approximate nonlinear functions, dynamic neural networks (DNNs) can be used to approximate the behavior of dynamic systems. DNNs distinguish themselves from static feedforward NNs in that they have at least one feedback loop and their representation is described by differential equations. Because of internal state feedback, DNNs are known to provide faster learning and exhibit improved computational capability in comparison to static feedforward NNs.;In this dissertation, a DNN architecture is utilized to approximate uncertain nonlinear systems as a means to develop identification methods and observers for estimation and control. In Chapter 3, an identification-based control method is presented, wherein a multilayer DNN is used in conjunction with a sliding mode term to approximate the input-output behavior of a plant while simultaneously tracking a desired trajectory. This result is achieved by combining the DNN-identification strategy with a RISE (Robust Integral of the Sign of the Error) controller. In Chapters 4 and 5, a class of second-order uncertain nonlinear systems with partially unmeasurable states is considered. A DNN-based observer is developed to estimate the missing states in Chapter 4, and the DNN-based observer is developed for an output feedback (OFB) tracking control method in Chapter 5. In Chapter 6, an OFB control method is developed for uncertain nonlinear systems with time-varying input delays. In all developed approaches, weights of the DNN can be adjusted on-line: no off-line weight update phase is required. Chapter 7 concludes the proposal by summarizing the work and discussing some future problems that could be further investigated.
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