Abstract
This paper presents the online control system application for improving the DC motor performance. DC motor widely used in industries and many appliances. For this aim fuzzy logic controller is applied. The type of fuzzy controller use is an incremental fuzzy logic controller (IFLC). The IFLC is developed by using MATLAB Simulink Software and implemented in online position control system applying RAPCON board as a platform. The experimental results produced the best gains of the IFLC are 1.785, 0.0056955 and 0.01 for error gain (GE), gain of change error (GCE) and gain of output (GCU) respectively. Its produce smaller rise time, peak time, 0% overshoot and smaller settling time. Beside that the IFLC response also able to follow the set point. The controller response parameters values are also acceptable. It means that the IFLC suitable to be use for improving the position control system performance.
Highlights
DC motors widely used to improve performance, such as industry, home appliances, robot manipulators etc. because it has high reliability, flexibility and low cost
Most DC motor driver applications are in position control or in speed control systems [1]
Several controllers have been applied for position control system with DC motor as a driver such as Proportional Integral Derivative (PID) [1], Fuzzy Logic [2,3], Neural Network (NN) [4] and etc
Summary
DC motors widely used to improve performance, such as industry, home appliances, robot manipulators etc. because it has high reliability, flexibility and low cost. Several controllers have been applied for position control system with DC motor as a driver such as Proportional Integral Derivative (PID) [1], Fuzzy Logic [2,3], Neural Network (NN) [4] and etc. PID controller is commonly applied for controlling motor because of it has simple structure and comprehensive control algorithms [1,2] It has been implemented in position control systems, but still suffer from poor performance due to non-linear parameters. There are kind of neural network with high efficiency and strong function generalizing in terms of learning speed and simplicity of the structure. The IFLC does not need knowledge model of system, complex structure, learning process and etc This controller work is based on the principle of human expert decision making in problem solving mechanism [2,3]. The Mamdani inference as a computational fuzzy inference type is used because its decision is more accurate
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