Back-Propagation Neural Networks in Adaptive Control of Unknown Nonlinear System
NAVAL POSTGRADUATE SCHOOL MONTEREY CA
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The objective of this research is to develop a Back-propagation Neural Network BNN to control certain classes of unknown nonlinear systems and explore the networks capabilities. The structure of the Direct Model Reference Adaptive Controller DMRAC for Linear Time Invariant LTI systems with unknown parameters is first analyzed. This structure is then extended using a BNN for adaptive control of unknown nonlinear systems. The specific structure of the BNN DMRAC is developed for the control of four general classes of nonlinear systems modelled in discrete time. Experiments are conducted by placing a representative system from each class under the BNNs control. The conditions under which the BNN DMRAC can successfully control these systems are investigated. The design and training of the BNN are also studied. The results of the experiments show that the BNN DMRAC works for the representative systems considered, while the conventional least-squares estimator DMRAC fails. Based on analysis and experimental findings, some general conditions required to ensure that this technique works are postulated and discussed. General guidelines used to achieve the stability of the BNN learning process and good learning convergence are also discussed. To establish this as a general and significant control technique, further research is required to establish analytically, the conditions for stability of the controlled system, and to develop more specific rules and guidelines in the BNN design and training.
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