23-03-2017, 02:46 PM
To design a speed controller of a DC motor by selecting a PID parameter using the genetic algorithm (GA) and the Adaptive Neurofuzzy Inference System (ANFIS). The DC motor could be represented by a nonlinear model when considering nonlinearities such as magnetic saturation. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. The model of a DC motor is considered as a third order system. And this paper compares three types of parameter setting methods for the PID controller. One is the design of the controller by the Ziegler and Nichols, the second is the design of the controller by the Genetic Algorithm method and the third is the design of the controller by the Adaptive Neurofuzzy Inference System (ANFIS). The proposed methods could be applied to higher order systems.
The development of high performance motor drives is very important in industrial applications as well as in other purpose applications. Generally, a high performance motor drive system must have a good tracking of dynamic speed commands and a load regulation response. DC motors are used in various applications, such as defense, industries, robotics, etc. DC units, because of their simplicity, ease of application, reliability and cost-favourable have long been a backbone of Industrial applications. The project provides the efficient and simple method to control the speed of the DC motor using the ATMEGA16 microcontroller and the L298N IC motor controller.
With the use of ATMEGA16 and l298N we can drive the dc motor at the desired speed with a feedback loop and in this project we have used the integral proportional and derivative method in which the errors are not only solved but also carried to their value Minimum with very little amount of error oscillations.
PROPORTIONAL CONTROL:
The proportional part of PID examines the magnitude of the error and reacts proportionally. A big mistake gets a big response
INTEGRAL CONTROL:
To address the first problem with proportional control, the integral control attempts to correct a small error (offset).
DERIVED CONTROL:
The part derived from the control output attempts to observe the rate of change in the error signal. The derivative will cause a greater system response at a faster rate of change than at a small rate of change.