Fuzzy Implementation of Model Reference Adaptive Control of Dc Drives

Now a days DC drives are being used in versatile variable speed applications in industries. A separately excited DC motor is taken as a plant. The dynamic behaviour of a DC motor varies with the moment of inertia, friction of the load, temperature etc. which are evident from the applications such as rolling mills, wiring machines and tracking telescope. Non-linearities cannot be neglected when the system works close to the rated conditions. The most important non-linearity in DC drive is the saturation of the magnetic core, which determines the non-linear growth of both the flux and voltage when the field current increases. Converter bridge also introduces some structural perturbations when the firing angle varies. Hence high performance control systems are needed mainly for two reasons. The first reason is concerned with the needs of the process, either in terms of steady state error or dynamic performance. The second one arises from the economic demand for the maximum utilization of costly power. All these pointing to the need of high performance control system. Intelligent, self-learning or self-organizing controls using expert systems, fuzzy logic and neural networks have been recently recognized as important tools to enhance the performance of power electronic systems. The combination of intelligent control with adaptive control appears today the most promising research accomplishment in the drive control area and in the meantime, as the best approach for the optimal exploitation of intelligent control and practical realization of adaptive motor drives. This paper deals with the conventional model reference adaptive control (MRAC) and replaces conventional control technique such as PI control with model reference fuzzy adaptive control (MRFAC) scheme . The Model Reference Adaptive Control (MRAC) speed control systems do not achieve consistent satisfactory performance over wide range of speed demand, especially at low speed and there is no defined rule to guide designers to choose the adaptation gains. The fuzzy logic model reference adaptive control maintains satisfactory response irrespective of the magnitude of the inputs. It enhances the performance of the DC drive compared to conventional MRAC. The performance of the drive system, thus obtained, is forming a set of test conditions with model reference fuzzy adaptive control. The performance of the drive is tested for load disturbances along with reference model. This work compares the performance of Model Reference Fuzzy Adaptive scheme over conventional MRAC. This work is carried out by using MATLABSIMULINK.