# Performance Evaluation of a Quadcopter by an Optimized Proportional-Integral-Derivative Controller
## 1 Introduction
Furthermore, it needs to be energized in order, as far as the rotational moment is generated. Paper [8] proposed a concept called electron commutation, that is, a circuit of the electronic governor, realized according to the electronic commutation, adjusts the speed and direction of the motor in real time according to a change in the control signal. According to the paper discussed above, the quadcopter UAV needs not only have four brushless DC motors, but also four electronic governors. Certainly, the control center is responsible for integration, that is, the flight controller is based on the work of [8] using \(\mathrm{Stm}32\mathrm{F}40\mathrm{7}\mathrm{V}\mathrm{G}\mathrm{T}\mathrm{ }\mathrm{G}\mathrm{T}\mathrm{6}\) for flight-controller development. In addition, the MCU uses an ARM-M4 processor, and the main frequency can reach up to 168 MHz. The single-chip system has high performance, which is suitable as a flight chip, and this component is similar to our human brain. Besides, it is responsible for each foreign message and signal action for the quadcopter UAV flight controller. In fact, the MCU is required to be equipped with a gyroscope and an accelerometer, and to use the data from these sensors to determine the angular velocity of the UAV. The law of conservation of angular momentum can be evaluated according to the obtained data when the vehicle is subjected to external forces. If the angular momentum changes, the total angular momentum will not change in any way under such conditions. This concept can be applied to UAV aircraft. In report [9], the gyroscope and accelerometer use MPU6050 components to measure the angle and angular velocity of this UAV. From the pieces of knowledge according to the above description, one can calculate the external force on the UAV by measuring the angle and angular velocity of the UAV [10; 11]. It could also follow up on the IMU (inertial measurement unit system) to know the error value of this application product. It is convenient to compare the error value with the target value, and let the quadcopter UAV compare this error value. The action of correcting the PID controller is an important indicator of the feedback system, through this value back to the input. The current article proposed a basic Optimized PID architecture shown in Figure 1, which runs the tool Simulink in Matlab(r), and is designed by the first-order differential equation. Such an equation can be used to verify the PID control algorithm and to eliminate the error, for example, \(K_{p}\), \(K_{i}\), and, \(K_{d}\). Accordingly, it is worth knowing that the above-mentioned parameters of \(K_{p}\), \(K_{i}\), and \(K_{d}\) could be adaptively adjusted by following up with the UAV location. Typically, the order of the control steps is first to adjust the proportional value \(k_{P}\) which is followed by the integral stage \(k_{i}\) modification, and the correction of the derivative parameter \(k_{P}\) will be carried out in the end, if necessary. Therefore, the proposed algorithms of the PID controller can be declared to have optimized performance, which can be noted as the main contributions of the investigation. For this reason, the commonly used and powerful PID control concept is used to filter the transformed error variables, that is, to optimize the performance of a UAV. A composite learning algorithm consisting of a neural network and a perturbation observer is incorporated to handle the error-induced nonlinear terms as the flight safety progresses [12]. A successful solution for eliminating path deviation in multi-terrain robots has been proposed and implemented in [13]. The deviation is almost negligible after the implementation of the proposed algorithm, and it provides fusing wheel encoder and inertial measuring unit (IMU) data for PID control using the ROS (robot operating system) packages.