Aspects Regarding Fly Control of Quadcopter

Quadcopter is one of Unmanned Aerial Vehicle (UAV) which has two pairs of identical fixed pitched rotor propellers. It can fly autonomously based on pre-programmed flight or manually controlled by a remote, and every movement achieved by varying the speed of each rotor independently. The orientation of quadcopter axes relative to a reference line and its direction of motion are known as attitude. Fly control factors are affected by attitude determination which can be calculated from 3 possible angles using combined measurement. Gyroscope and accelerometer are primary sensors to control quadcopter attitude, but magnetometer sensor and GPS also used to enhance the stability during flight. This paper will focus on details of function and mathematical formula of every factor regarding fly control and comparative data of 2 types of orientation sensor used in this system

PID Controller Tuning Optimization with Genetic Algorithms for a Quadcopter

This paper is focused on the dynamic of mathematical modeling, stability, nonlinear gain control by using Genetic algorithm, utilizing MATLAB tool of a quadcopter. Previously many researchers have been work on several linear controllers such as LQ method; sliding mode and classical PID are used to stabilize the Linear Model. Quadcopter has a nonlinear dynamics and unstable system. In order to maintain their stability, we use nonlinear gain controllers; classical PID controller provides linear gain controller rather than nonlinear gain controller; here we are using modified PID control to improve stability and accuracy. The stability is the state of being resistant to any change. The task is to maintain the quadcopter stability by improving the performance of a PID controller in term of time domain specification. The goal of PID controller design is to determine a set of gains: Kp, Ki, and Kd, so as to improve the transient response and steady state response of a system as: by reducing the overshoot; by shortening the settling time; by decrease the rise time of the system. Modified PID is the combination of classical PID in addition to Genetic Algorithm. Genetic algorithm consists of three steps: selection, crossover, and mutation. By using Genetic algorithm we correct the behavior of quadcopter

Hard iron distortion compensation for 3 axis magnetometer

This paper presents the way how the hard iron effect could be compensated and a way to implement it on a small power device such as a microcontroller. Because of the magnetized materials that can stay near a magnetometer sensor and because of the very small magnetic field of the Earth, before the use of the measured values from a magnetometer to determine the heading (angle with the N direction) of the sensor a compensation is needed

IoT Devices Signals Processing based on Multi-dimensional Shepard Local Approximation Operators in Riesz MV-algebras

In this article we continue the study started in [8] to use Riesz MValgebras for IoT devices signals processing. The Shepard local approximation operators introduced in [8] were defines such that to approximate single variable functions. In real industrial usage, the signals coming from IoT devices may be influenced by mode than a parameter, and thus we introduce generalized Shepard local approximation operators that can approximate multi-dimensional functions and some numerical experiments are considered

PID and Fuzzy-PID Control Model for Quadcopter Attitude with Disturbance Parameter

This paper aims to present data analysis of quadcopter dynamic attitude on a circular trajectory, specifically by comparing the modeling results of conventional Proportional Integral Derivative (PID) and Fuzzy-PID controllers. Simulations of attitude stability with both control systems were done using Simulink toolbox from Matlab so the identification of each control system is clearly seen. Each control system algorithm related to roll and pitch angles which affects the horizontal movement on a circular trajectory is explained in detail. The outcome of each tuning variable of both control systems on the output movement is observable while the error magnitude can be compared with the reference angles. To obtain a deeper analysis, wind disturbance on each axis was added to the model, thus differences between each control system are more recognizable. According to simulation results, the Fuzzy-PID controller has relatively smaller errors than the PID controller and has a better capability to reject disturbances. The scaling factors of gain values of the two controllers also play a vital role in their design

Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing

The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks

Quadcopter thrust optimization with ducted-propeller

In relation to quadcopter body frame model, propeller can be categorized into propeller with ducted and without ducted. This study present differences between those two using CFD (Computational Fluid Dynamics) method. Both categories utilize two blade-propeller with diameter of 406 (mm). Propeller rotation generates acceleration per time unit on the volume of air. Based on the behavior of generated air velocity, ducted propeller can be modeled into three versions. The generated thrust and performance on each model were calculated to determine the best model. The use of ducted propeller increases the total weight of quadcopter and also total thrust. The influence of this modeling were analyzed in detail with variation of angular velocity propeller from 1000 (rpm) to 9000 (rpm). Besides the distance between propeller tip and ducted barrier, the size of ducted is also an important part in thrust optimization and total weight minimization of quadcopter

Aspects Regarding Fly Control of Quadcopter

Quadcopter is one of Unmanned Aerial Vehicle (UAV) which has two pairs of identical fixed pitched rotor propellers. It can fly autonomously based on pre-programmed flight or manually controlled by a remote, and every movement achieved by varying the speed of each rotor independently. The orientation of quadcopter axes relative to a reference line and its direction of motion are known as attitude. Fly control factors are affected by attitude determination which can be calculated from 3 possible angles using combined measurement. Gyroscope and accelerometer are primary sensors to control quadcopter attitude, but magnetometer sensor and GPS also used to enhance the stability during flight. This paper will focus on details of function and mathematical formula of every factor regarding fly control and comparative data of 2 types of orientation sensor used in this system

Flight Stability Analysis of a Symmetrically-Structured Quadcopter Based on Thrust Data Logger Information

Quadcopter flight stability is achieved when all of the rotors–propellers generate equal thrust in hover and throttle mode. It requires a control system algorithm for rotor speed adjustment, which is related with the translational vector and rotational angle. Even with an identical propeller and speed, the thrusts generated are not necessarily equal on all rotors–propellers. Therefore, this study focuses on developing a data logger to measure thrust and to assist in flight control on a symmetrically-structured quadcopter. It is developed with a four load cells sensor with two-axis characterizations and is able to perform real-time signal processing. The process includes speed adjustment for each rotor, trim calibration, and a proportional integral derivative (PID) control tuning system. In the data retrieval process, a quadcopter was attached with data logger system in a parallel axis position. Various speeds between 1200 rpm to 4080 rpm in throttle mode were analyzed to determine the stability of the resulting thrust. Adjustment result showed that the thrust differences between the rotors were less than 0.5 N. The data logger showed the consistency of the thrust value and was proved by repeated experiments with 118 s of sampling time for the same quadcopter control condition. Finally, the quadcopter flight stability as the result of tuning process by the thrust data logger was validated by the flight controller data