POSITION CONTROL OF OMNIDIRECTIONAL AUTOMATED GUIDED VEHICLE USING PID AND FUZZY-PID COMPENSATOR
According to World Robotics Industrial Robots 2018 report by International Federation of Robotics, demand for automated guided vehicle (AGV) has a market value of 0.5 billion dollars in 2018 and is projected to become 0.9 billion in 2021. An autonomous AGV needs a motion controller module that co...
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Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/57915 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | According to World Robotics Industrial Robots 2018 report by International Federation of
Robotics, demand for automated guided vehicle (AGV) has a market value of 0.5 billion
dollars in 2018 and is projected to become 0.9 billion in 2021. An autonomous AGV needs a
motion controller module that consists of position and velocity controller. A conventional
approach to develop this module is to employ a PID-based controller. However, the ideal and
linear form of PID controller may not produce satisfying result if the plant is not accurately
modelled or the plant itself has a dominant nonlinear behavior. Several compensation
techniques have been developed to counter this problem, one example of them is by using
fuzzy controller.
In the case of AGV development by PT. Irostech Solusi Intelijen, only the kinematic AGV
model is available. Hence, PID-based motion controller is not enough to mitigate the error of
AGV’s nonlinear movements such as curving. Therefore, in this final project an in-depth
study of implementation of a fuzzy controller to compensate PID controller is conducted. The
algorithm is developed inside Robot Operating System (ROS) framework in order to integrate
it with the rest of AGV system, while the codes themselves are developed using python in
order to ease the development phase.
In this final project, the control algorithm has been developed and implemented in Robot
Operating System framework. The two types of controllers have been also tested using
Gazebo simulation and compared. It is recommended to implement the fuzzy compensator if
the company wants more responsive behavior and faster error reduction but for pure
trajectory tracking a conventional PID is enough.
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