Adaptive Self-Tuning Neuro Wavelet Network Controllers

Single layer feedforward neural networks with hidden nodes of adaptive wavelet functions (wavenets) have been successfully demonstrated to have potential in many applications. Yet applications in the process control area have not been investigated. In this paper an application to a self-tuning design method for an unknown nonlinear system is presented. Different types of frame wavelet functions are integrated for their simplicity, availability, and capability of constructing adaptive controllers. Infinite impulse response (IIR) recurrent structures are combined in cascade to the network to provide a double local structure resulting in improved speed of learning. In particular, neuro-based controllers assume a certain model structure to approximate the system dynamics of the "unknown" plant and generate the control signal. The capability of neuro-controllers to self-tuning of an unknown nonlinear plants is then illustrated through design examples. Simulation results demonstrate that the self-tuning design methods are directly applicable for a large class of nonlinear control systems. Acknowledgments iii Acknowledgments I would like to express my sincere gratitude to my advisor, Dr. Hugh F. VanLandingham for his support, advice, and guidance throughout my Ph.D. research at Virginia Tech. I thank to Dr. VanLandingham for introducing me to the area of Artificial Neural Networks and for fostering an atmosphere of openness, creativity, and support. I am also very grateful to Dr. VanLandingham for many reviews and proofread of the materials. I would like to thank Dr. Richard L. Moose and Dr. Kenneth Hannsgen for their valuable time to serve on my Ph.D. advisory committee. Their attentive responses and suggestions toward my dissertation are sincerely appreciated. Grateful ac..