Abstract
During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of the fuzzy set theory, especially in the realm of the industrial processes, which do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the input-output relations i.e., accurate mathematical models. The fuzzy logic controller based on wavelet network provides a means of converting a linguistic control strategy based on expert knowledge into an automatic strategy. In the available literature, one can find scores of papers on fuzzy logic based controllers or fuzzy adaptation of PID controllers. However, relatively less number of papers is found on fuzzy adaptive control, which is not surprising since fuzzy adaptive control is relatively new tool in control engineering. In this paper, fuzzy adaptive PID controller with wavelet network is discussed in subsequent sections with simulations. An adaptive neural network structure was proposed. This structure was used to replace the linearization feedback of a second order system (plant, process). Also, in this paper, it is proposed that the controller be tuned using Adaptive fuzzy controller where Adaptive fuzzy controller is a stochastic global search method that emulates the process of natural evolution. It is shown that Adaptive fuzzy controller be capable of locating high performance areas in complex domains without experiencing the difficulties associated with high dimensionality or false optima as may occur with gradient decent techniques. From the output results, it was shown that Adaptive fuzzy controller gave fast convergence for the nonparametric function under consideration in comparison with conventional Neural Wavelet Network (NWN).