In this work, the soft computing techniques based intelligent model reference adaptive controller was explained with proposed method. The intelligent supervisory loop is added to the conventional Model Reference Adaptive controller (MRAC) function. In conventional MRAC method, the controller is designed to realize the system output response to the reference model output response based on the system performance, which is followed linearity. This method is effective for controlling and stability of linear systems with unknown parameters. However, using MRAC to control and stability analysis of nonlinear systems with real time is a difficult one. In this paper, the soft computing techniques based intelligent model reference adaptive controller is proposed to overcome the problems. The soft computing techniques based intelligent model is used to compensate the non linearity of the system that is not considered in the conventional MRAC. The parallel neural network controller was designed and simulated to precisely tracking the system output responses to the desired commend trajectory for stability analysis. The proposed soft computing techniques based intelligent model reference adaptive controller model was implemented and minimize the error between the model and system output response. The effectiveness of the proposed control scheme for stability analysis is demonstrated by simulation and compared with existing analysis.
