Adaptive self-tuning neuro wavelet network controllers

Single layer feedforward neural networks with hidden nodes of adaptive wavelet functions (wavenet) have been successfully demonstrated with potential in many applications. Yet applications in the control process area have not been investigated. In this paper, an application to a self-tuning design method of 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 structure is combined by cascading to the network to provide double local structure resulting in improving speed of learning. Particular neuro-based controllers assume a certain model structure to approximately identify the system dynamics of the "unknown" plant and generate the control signal. The capability of neuro-controllers to self-tuning of 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.