Speed Control of Separately Excited D.C Motor using Self Tuned ANFIS Techniques

This paper presents a comparison of performance of controllers such as PI, PID controller, Self tuned fuzzy controller, G.A Fuzzy PID & Self Tuned ANFIS, for DC motor speed control. Simulation results have demonstrated that the use of Self Tuned ANFIS results in a good dynamic behaviour of the DC motor, a perfect speed tracking with no overshoot, gives better performance and high robustness than those obtained by use of the other controllers. With the development of power electronics resources, the direct current machine has become more and more useful. The speed of DC motor can be adjusted to a great extent as to provide easy controllability and high performance. There are several conventional as well as intelligent controllers to control the speed of DC motor such as: PID Controller, Fuzzy Logic Controller, G.A , Neuro-Fuzzy Controller etc. The Adaptive Neuro-Fuzzy Inference System (ANFIS), developed in the early 90s by Jang, combines the concepts of fuzzy logic and neural networks to form a hybrid intelligent system that enhances the ability to automatically learn and adapt. Hybrid systems have been used by researchers for modeling and predictions in various engineering systems. The basic idea behind these neuro-adaptive learning techniques is to provide a method for the fuzzy modeling procedure to learn information about a data set, in order to automatically compute the membership function parameters that best allow the associated FIS to track the given input/output data. The membership function parameters are tuned using a combination of least squares estimation and back-propagation algorithm for membership function parameter estimation. These parameters associated with the membership functions will change through the learning process similar to that of a neural network. Their adjustment is facilitated by a gradient vector, which provides a measure of how well the FIS is modeling the input/output data for a given set of parameters. Once the gradient vector is obtained, any of several optimization routines could be applied in order to adjust the parameters so as to reduce error between the actual and desired outputs. This allows the fuzzy system to learn from the data it is modeling. The approach has the advantage over the pure fuzzy paradigm that the need for the human operator to tune the system by adjusting the bounds of the membership functions is removed.