Abstract
This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.
Highlights
In the 4th industrial revolution era, the application of multi-copters has significantly expanded, and it has attracted the interest of many researchers specialized in multi-copter control engineering
According to the above discussions, this study presents a method for auto-tuning PID parameters using multilayer fuzzy neural network (PID-MFNN) to control the attitude the quadcopters
The 3D Robotics IRIS quadcopter was used without additional sensors, which were simulated on the basic terrain as a real world environment for verifying our designed control system
Summary
In the 4th industrial revolution era, the application of multi-copters has significantly expanded, and it has attracted the interest of many researchers specialized in multi-copter control engineering. Multi-copters need to maintain accurate attitudes to ensure stable flight, so the development of accurate and stable controllers for multi-copters is essential. Since PID is a linear controller, it is generally hard to use to achieve the highest control performance for non-linear control systems (Sarabakha et al, 2017). In the design of a PID controller, it is necessary to obtain the exact mathematical model of the control system and to optimize the gain value to achieve the desired performance. This work requires complexity calculations and an accurate modeling plant
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