A disturbance signal was also applied to the input of the control system. Then a fuzzy proportional-derivative (FPD) controller was designed and system responses of FPDs with different defuzzification methods were investigated. ![]() First, a crisp proportional-derivative (PD) controller was designed and tuned using a Simulink block instead of conventional tuning methods such as hand-tuning or Ziegler-Nichols frequency response method. ![]() The motor was modelled and converted to a subsystem in Simulink. Have been presented the obtained results are promising and is likely to be utilized by the industries.Ī fuzzy control system to control the position of a DC motor. One is the motor error speed between the reference and actual speed, other is change in speed (speed error derivative).In this paper a comparison among P, PI, PID and fuzzy logic controller through MATLAB/Simulink software. The Fuzzy Logic controller is structural according to Fuzzy rule base such that the system are fundamentally robust, therefore Fuzzy system in 25 Fuzzy rule base, the Fuzzy Logic controller has two inputs. Since, classical controllers like P, PI and PID are failing to control the drive when weight parameters be also changed, Fuzzy logic controller gives superior speed manage result at such condition. In this paper a separately excited dc motor using MATLAB modeling, has been designed whose speed may be investigated using the Proportional, Integral, Derivative (K P, K I, K D) gain of the PID controller. DC motor is widely used in many applications like steel mills, electric trains, cranes and much more. The results obtained by using a conventional PID controller and the designed Fuzzy Logic Controller have been studied and compared. The membership functions and the rules have been defined using the FIS editor given in MATLAB. ![]() The errors are evaluated according to the rules in accordance to the defined member functions. These are: The input speed error denoted by Error (e), the input derivative of speed error denoted by Change of error (∆e), and The output frequency denoted by Change of Control (wref) (Varun et al., 2012). ![]() These rules portray a relationship between two inputs into the controller and one output, all of which are normalized voltages. The advantages of Fuzzy Logic Controllers (FLCs) over the conventional controllers are: they are economically advantageous to develop, a wider range of operating conditions can be covered using FLCs, set of rules is incorporated into the design, resulting in a non-linear controller with improved large signal performance over linear PID controllers and an initial approximate set of fuzzy rules can be impulsively refined by a self-organizing fuzzy controller. The designed Fuzzy Logic Controller’s performance is compared against that of a PID controller, the result indicates the best possible tuning of simulation with FLC controller, the peak overshoot and Settling time result obtained shows that the fuzzy logic controller has an excellent controllability over the speed of three-phase induction motor compare to when PID controller is employed. Reason being that they are sensitive to drive parameter variations and performance may deteriorate. An induction motors are characterized by complex, highly non-linear and time-varying dynamics, and hence their speed control becomes challenging if conventional controllers are used. This paper describes work which investigates application of fuzzy logic controller to control the speed of induction motors used in HDPE (High-Density Polyethylene) extrusion industry, which is one of the artificial intelligent methods of controlling nonlinear systems like the electric motor drive use in an extrusion Industry.
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