Non-linear dynamic movement of quadcopter was analyzed using position tracking method in a circular trajectory. The result was used to obtain control system algorithm which can enhance maneuver ability. The control system was modelled into feedback linearization and LQR (Linear Quadratic Regulator) controller, both were used to stabilize quadcopter attitude in the trajectory. Quadcopter behavior while using feedback linearization modeling and LQR control were clearly seen in pitch-roll position in circular trajectory and at the time of bounded disturbance. Wind parameter appraisal was added to boost the robustness. To get optimal result, overall control system was tuned with PID (Proportional Integral Derivative) loop control. The study showed that adjustment of the weight variable on LQR and coefficient on PID resulted in better stability and performance of quadcopter dynamic movement. Overall, control algorithm for modeling had the capability to reject disturbance although small error still occurred in the output trajectories tracking.

This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.

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.

An unbalanced propeller can affect a quadcopter’s performance due to vibration, also decreasing its thrust’s yield. Analyses were conducted to determine the correlation between vibration in the dynamic movement of a balanced and an imbalanced propeller using a laser vibrometer (portable digital vibrometer) PDV-100, to determine the correlation between propeller rotational speed and its vibration, as well as to determine the propeller’s maximum rotational speed to avoid over-vibration. The vibration analysis was conducted on a carbon fiber 2-blade propeller by comparing the results of vibration tests with the propeller blade contour. The vibration response of the propeller has been analyzed at three points, respectively the hub, the center, and the tip of the blade, to determine the point having the largest value of the vibration on the propeller running at a maximum rotation speed of 7000 rpm. The same analysis was made on two propellers: the first one of type 1340 with a diameter of 13 inches and the second one of type 1447 with a diameter of 14 inches. The vibration was reduced by propeller’s static balancing, thus increasing the propeller stability. The result showed that an imbalanced propeller generated a decrease in the rotational speed and higher vibration values compared to the balanced propeller. The vibration values showed a linear dependency to the rotor speed; the higher the speed, the bigger the vibration. The limit of rotational speed for the balanced model of the 1340 type propeller was 5000 rpm whilst the corresponding value for the 1447 type propeller was 4500 rpm. Finally, the result was used to optimize the propeller’s overall performance.

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.