Abstract
In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.
Highlights
DC motors is very common devices in different industrial applications and autonomous systems, such as disk drives, automatic doors, robotic arms and numerous applications
Medina-Santiago, et al, proposed the Neural Network (NN) controller for mobile robot performing detection of obstacle by using multilayer perceptron trained by MATLAB, and implementation neural control on microcontroller (Arduino), DC motors and ultrasonic sensors [6]
The aim of this work is design a classical PID and adaptive inverse neural network controllers in order to control on speed and position of DC motor
Summary
DC motors is very common devices in different industrial applications and autonomous systems, such as disk drives, automatic doors, robotic arms and numerous applications. Suitable control of these two outputs lead increasing system's efficiency In several applications, such as robotic and disk drives, control on position is more substantial than control on speed [1]. The aim of this work is design a classical PID and adaptive inverse neural network controllers in order to control on speed and position of DC motor. The adaptive inverse neural network controller detraction the effect of nonlinear parameter and produce efficient response for control system. This type was chosen to implementation on FPGA Spartan 3A from Xilinx. The simulation results are displayed in Part: 6, Part: discuss the hardware implementation and part: 8 civilize the conclusion
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