06-09-2017, 11:40 AM
The speed of the DC motor is controlled by the PID controller and the fuzzy logic controller. The PID controller requires a mathematical model of the system while the controller of fuzzy logic is based on experience through rule-based knowledge. The fuzzy logic controller design requires many design decisions, eg rule base and fuzzification. The FLC has two inputs, one of these inputs is the speed error and the second is the change in the speed error. There are 49 fuzzy rules that are designed for the fuzzy logic controller. The center of gravity method is used for defuzzification. The fuzzy logic controller uses the Mamdani system which uses fuzzy sets in the consequent part. The PID controller chooses its parameter base in the test and error method. PID and FLC are investigated with the help of the simulation of the MATLAB / SIMULINK package program. It is believed that the FLC is more difficult in design compared to the PID controller, but has an advancement to be more suitable to satisfy the non-linear characteristics of the DC motor. The results show that fuzzy logic has minimum transient and steady-state parameters, which shows that FLC is more efficient and efficient than the PID controller.
The fuzzy logic controller is designed according to fuzzy rules so that systems are fundamentally robust. There are 25 fuzzy rules for auto-tuning each PID controller parameter. The FLC has two entries. One is the engine speed error between the reference speed and the actual speed and the second is the change in speed error (derived from speed error). Second, the output of the FLC, ie the parameters of the PID controller are used to control the speed of the DC motor. The study demonstrates that both the exact characters of the PID controllers and the flexible characters of the fuzzy controller are present in the fuzzy self-matching PID controller. The diffuse self-tuning approach implemented in a conventional PID structure was able to improve the dynamics as well as the static response of the system. The comparison between the conventional output and the fuzzy self-tuning output was performed based on the simulation result obtained by MATLAB. The simulation results demonstrate that the self-tuned PID controller designs a good dynamic DC motor behavior, perfect speed tracking with less settling time and minimum, minimum overrun, minimum steady state error and better performance compared to conventional PID controller.
The fuzzy logic controller is designed according to fuzzy rules so that systems are fundamentally robust. There are 25 fuzzy rules for auto-tuning each PID controller parameter. The FLC has two entries. One is the engine speed error between the reference speed and the actual speed and the second is the change in speed error (derived from speed error). Second, the output of the FLC, ie the parameters of the PID controller are used to control the speed of the DC motor. The study demonstrates that both the exact characters of the PID controllers and the flexible characters of the fuzzy controller are present in the fuzzy self-matching PID controller. The diffuse self-tuning approach implemented in a conventional PID structure was able to improve the dynamics as well as the static response of the system. The comparison between the conventional output and the fuzzy self-tuning output was performed based on the simulation result obtained by MATLAB. The simulation results demonstrate that the self-tuned PID controller designs a good dynamic DC motor behavior, perfect speed tracking with less settling time and minimum, minimum overrun, minimum steady state error and better performance compared to conventional PID controller.