Intelligent control of DC motor using GA Based Hybrid Fuzzy Controller

: In this paper we used transfer function of second order of separately excited dc motor and compare their performance of speed regulation using PID controller and GA based hybrid fuzzy controller. In this paper our approached to tune the fuzzy logic controller using genetic algorithm. Fuzzy logic controller tuned by the lesser number of design parameter and all the control strategies utilize. The output speed error and its derivative as feedback damping signals. Through this simulation the performance of the GA optimized Fuzzy Logic Controller is compared with that of the conventional PID controller. The GA optimized FLC gives better performance in terms of delay time, peak time, steady state error, integral absolute error (IAE). Keywords— DC motor, fuzzy controller, genetic Algorithm, Membership Functions (MF’s), Universe of Discourse (UOD), Speed Regulation. Introduction performance motor drives are very important in High industrial as well as other purpose applications such as steel rolling mills, electric trains and robotics. Generally, a high performance motor drives system have good dynamic response which perform speed command tracking and load regulating response [1]. DC drives have simplicity, ease of application, flexibility, high reliability and favorable cost The controllers that desired to control the speed of DC motor are of several conventional and numeric types e.g. Proportional Integral (PI), Proportional Integral Derivative (PID), Fuzzy Logic Controller (FLC) or combinations of them e.g. FuzzyNeural Networks, Fuzzy-Genetic Algorithm, Fuzzy-Ants Colony, Fuzzy-Swarm. The Proportional Integral Derivative (PID) controller operates in majority of the control systems in the world [9]. It has been reported that more than 95% of the controllers in the industrial process control applications are of PID type as no other controller matches the simplicity, clear functionality, applicability and ease of use offered by the PID controller. PID controllers provide robust and reliable performance for most systems if the PID parameters are tuned properly II. Modelling of DC motor Consider a Separately Excited DC motor whose electric circuit of the armature and the free body diagram of the rotor are shown in Figure 2.2 Fig. 1: Schematic Representation of the Considered DC Motor The rotor and the shaft of Separately Excited DC motor are assumed to be rigid. Consider the following values for the physical parameters [11] Moment of inertia of the rotor J = 0.01 kg. m Damping (friction) of the mechanical system b = 0.1 . . N m s Electromotive force constant K = 0.01 Nm/A Electric resistance R = 1 Ω Electric inductance L = 0.5 H Rated speed 1000 r.p.m The input is the armature voltage V in Volts (driven by a voltage source). Measured variables are the angular Velocity Alok RanjanInternational Journal of Computer Science information and Engg., Technologies ISSN 2277-4408 || 01012013-001 IJCSIET-ISSUE3-VOLUME1-SERIES1 Page 2 of the shaft in radians per second, and the shaft angle in radians. The motor torque, T, is related to the armature current, i, by a constant factor K T Ki  ..........(1) The back electromotive force (emf), Vb, is related to the angular velocity by ......(2) b W d V K dt   From Figure 2.4 we can write the following equations based on the Newton‟s law combined with the Kirchhoff‟s law. 2 2 ......(3) d d J b K dt dt     .......(4) di d L Ri V K dt dt     Using the Laplace transform, equations (3) and (4) can be written as: 2 ( ) ( ) ( ).......(5) Js s bs s KI s     ( ) ( ) ( ) ( )....(6) LsI s RI s V s Ks s     Where s denotes the Laplace operator. From (6) we can