Fuzzy logic based position control system design for nano quadcopter

we will present the design and novel deployment of Fuzzy PID (FPID) controllers to solve the position control of the nano quadcopter Crazyflie 2.0 in a real-world environment. In this way, the existing nano-quadcopter can be adapted to the desired situation. This modification affects the dynamic parameters of the system and causes the control structure to be changed. As a first stage, we have extracted a mathematical model that describes the dynamics of the quadcopter. Comprehensive literature reviews and different methods have been found for these mathematical processes such as Tait-Bryan and Quaternion are two different mathematical models. Tait-Bryan method was chosen for the thesis. Behaviors of Crazyflie 2.0 nano quadcopter like altitude and position controllers are designed in this determined mathematical model. The mathematical model was developed on the basis of quadcopter system dynamics using a linear and nonlinear approach. The rotation and rotation dynamic parameters of the quadcopter system were set. PID control, known as conventional method, is the most commonly used control method in quadcopters. In this context, the design and deployment of a Fuzzy PID (FPID) controller structure has been presented to solve the position control problem in the nano quadcopter used in the thesis. The basis of the FPID control structure is to rely on fuzzy logic foundations. Thanks to the expert knowledge input, the values of the membership functions are determined to the most appropriate points and the desired control structure is created. The testing of this control structure was done by testing on the Crazyflie 2.0 mathematical model created in the computer environment. Thanks to the same test mechanism, the PID coefficients as the traditional method have been determined and tested at the most appropriate values. In this way, a control system for the Crazyflie 2.0 unmanned aerial vehicle (UAV) is intended to be developed. The image detection based camera system has been added to ensure that this improved control system provides accurate and stable feedback. This added camera system is usually presented in an experimental environment where a low-cost camera system is implemented instead of the OptiTrack or VICON camera system used to detect robots like quadcopters. Unlike costly camera systems such as VICON and OptiTrack, this camera system is more suitable for cost and usage. Even only the camera system is not used on the computer so that the low-cost is selected. Such high cost camera systems are exhausting computers in terms of processing load. In this context, a low-cost Logitech C920 camera was used. Next, a fuzzy logic-based position controller structure is presented using the image-based positioning system, followed by Crazyflie 2.0, a low-cost camera. Furthermore, the image processing methods applied to the camera system are built in the open-source Python program structure. Crazyflie 2.0 and other quadcopters are a multi-input multi-output system considering the dynamic structure and control. This shows that there are coupled parameters on the model. Because of these reasons, the use of a low-cost camera may result in feedback uncertainties, the FPID controllers are designed to result in a robust and satisfying control system performance in the presence of noise and the highly coupled Crazyflie 2.0 nonlinearities. Using this camera image processing methods, the Crazyflie 2.0 detects the nano quadcopter's position in pixels for the two experiments, the z-axis was kept constant and only the error signal for the two axes were examined. The other two experiments are reference changes. The z axis is also variable during the reference change. The z-axis, which is the variable, has increased and decreased in a certain height range. This z-axis, which increases or decreases with specific examples, is periodic. In this way, both the PID and FPID can examine the behavior of the same examples. This comparison is made by measuring error performance. For the measurement, both the control structure FPID and the PID 's ISE error values, different reference points and reference trajectory results were collected and compared with each other. In this way, comparative experimental results showing that the obtained performance of the FPID structure is superior at various operating points and showing that it is more robust to noise and nonlinear compared to PID is presented.