Andi Dharmawan

Quadrotor flight stability system with Routh stability and Lyapunov analysis

UAV (Unmanned Aerial Vehicle) can fly autonomously or be controlled remotely by a pilot. Quadrotor as one type of UAV has been widely implemented in various needs. Its system design has a lot of control techniques involved. The design starts with the physical modeling. Quadrotor physical modeling is modeling based on the laws of physics as a theory and mathematical modeling of physical interpretation. The problem arises when actual plants are not fit with mathematical models that are used as the control design before. Such discrepancy arises because of external interference, plant parameters, and dynamics models that are nonlinear. If control systems are not designed to deal with non-linear interference, it is difficult to us to maintain quadrotor flight. Therefore, we need control methods that can be applied to linear and nonlinear systems. Routh Stability can be used to generate PID (Proportional Integral and Derivative) constants as a linear control method by using a Ziegler-Nichols. Lyapunov as a meth...

The use of video processing for quadrotor flight stability control monitoring

The quadrotor is one kind of Unmanned Aerial Vehicles (UAV). Quadrotor has the ability to hover with minimal translational velocity approaching a stationary state. This capability is supported by the four rotors. These rotors are used to lift the Quadrotor to fly. These rotors are placed on all four sides of the tip of Quadrotor. In order to fly with good stability, we can use an IMU sensor (Inertial Measurement Unit). Imu sensor consists of some DOF (degrees of freedom) sensors, such as 3-axis accelerometer sensor, 3-axis gyroscope sensor, 3-axis magnetometer sensor, and so on in accordance with the needs of flight. To test the stability of Quadrotor can be done by utilizing the video and image processing methods. This processing act as the ’eyes’ of Quadrotor. Sobel method as one of the image processing algorithms can be used to read the edges of the object. This method can measure the level of stability fly. But before reading the results of the edge must first be converted to black and white format. O...

Optimizing control based on fine tune PID using ant colony logic for vertical moving control of UAV system

Fixed-wing UAV has the ability to fly like a plane. For the movement of the flight, a good control system is needed. Part of the flight controls that require our attention among them is the vertical moving control. The controls are used to adjust the vertical movement involving aircraft pitch angle control. To control it, we can use control method that has been commonly used. The control method is a PID (Propositional, Integral and Derivative) control method. PID has three components (Kp, Ki, and Kd). The three components of PID can be obtained in various ways or methods. However, to produce a robust control, a method that can optimize the PID components is needed. Ant Colony Optimization (ACO) is one of PID controller optimization method which adapted by ant colony ability to find the shortest way from their nest to food. Some ACO parameters are a number of ants, parameters, and pheromone component for pheromone. Pheromones are the values given by the ants when they use the road.

PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization

Quadrotor as one type of UAV have the ability to perform Vertical Take Off and Landing (VTOL). It allows the Quadrotor to be stationary hovering in the air. PID (Proportional Integral Derivative) control system is one of the control methods that are commonly used. It is usually used to optimize the Quadrotor stabilization at least based on the three Eulerian angles (roll, pitch, and yaw) as input parameters for the control system. The three constants of PID can be obtained in various methods. The simplest method is tuning manually. This method has several weaknesses. For example if the three constants are not exact, the resulting response will deviate from the desired result. By combining the methods of PID with fuzzy logic systems where human expertise is implemented into the machine language is expected to further optimize the control system.

Optimizing control based on ant colony logic for Quadrotor stabilization

Quadrotor as one type of UAV (Unmanned Aerial Vehicle) have the ability to perform Vertical Take Off and Landing (VTOL). It allows the Quadrotor to be stationary hovering in the air. PID (Proportional Integral Derivative) control system is one of the control methods that are commonly used. It is usually used to optimize the Quadrotor stabilization at least based on the three Eulerian angles (roll, pitch, and yaw) as input parameters for the control system. The three constants of PID can be obtained in various ways or methods. However, to produce a robust control, we need a method that can optimize the PID components. Ant Colony Optimization (ACO) is one of PID controller optimization method which adapted by ant colony ability to find the shortest way from their nest to food. Some ACO parameters are number of ants, parameters, and pheromone constant for pheromone. Pheromones are the values given by the ants when they use the road.

Sistem Kendali Gimbal 2-Sumbu Sebagai Tempat Kamera Pada Quadrotor Menggunakan PID Fuzzy

The function of c amera gimbal control system that use in this research is to serves with the angle changes that occur due quadrotor maneuver. The PID control with tuning classical method has weakness , which is the PID variable not independently adjust to the environment , thus proposed using PID fuzzy control . Gimbal camera used in this study has a mechanical design with two joint (pitch and roll) and the BLDC motor as actuator . The angle c hanges that occur in the pitch and roll axis will be a feedback system . Then, fuzzy logic will tune the PID variable based on that feedback. Results of testing the system on 2-axis gimbal camera shows the PID fuzzy control generates better response in parameter risetime, overshoot, and settlingtime compared with PID control. Error input value range of [-30° 30°] and delta error of [-10° 10°] on the pitch and roll axes. The range of the output value for the pitch axis is, Kp at [40.2 46.2], Ki at [10.7 20.7], and Kd of [0.05 to 0.15]. The range of the output value for the roll axis is , Kp at [6.4 16.4], Ki at [17.3 to 27.3], and Kd at [0.08 0.16]. Speed response speed of pitch axis is 0.12 second and the roll axis is 1.07 seconds.

Optimasi Kendali PID menggunakan Algoritma Genetika untuk Penerbangan Quadrotor

Quadrotor is square-form unmanned aerial vehicle (UAV) type with four motor in each arms. Quadrotor has ability to take-off and landing vertically. This research designs and creates a system that capable to stabilize the quadrotor flight also able to maintain roll, pitch and yaw angle using PID controller optimized by genetic algorithm , one of evolutionary algorithms . PID is a common applied controller including to control the quadrotor. Tunning or setting PID parameter process is needed to obtain fit PID parameters. Tunning is very important to reach quadrotor flight stability. This research applies Ziegler-Nichols tunning to obtain PID parameters. Then the PID parameters will be a reference for genetic algorithm optimization process to obtain the suitest PID parameter to control roll, pitch ,and yaw angle. Optimization process result show quadrotor controller capable to reach stability with steady state error for pitch angle in range 2,34 degree conterclockwise to 3,37 degree clockwise, for roll angle in range 2,99 degreee counterclockwise to 2,27 degree clockwise, and for yaw angle in range 8,39 degree counterclockwise to 3,89 degree clockwise.

Sistem Kendali Penghindar Rintangan Pada Quadrotor Menggunakan Konsep Linear Quadratic

Quadrotor is one of UAV (Unmanned Aerial Vehicle) rotary wing aircraft type. Quadrotor has been widely used for various needs to military or civilian. Quadrotor can be operated manually by remote or autonomously. One of the difficulties of quadrotor operations is to avoid the obstacles before autonomous flying towards destination point. Therefore, an obstacle avoidance control system is required on quadrotor systems. Linear Quadratic Regulator is a control system that produces an input value system from state value and feedback. State value is produced from translation and rotation. That input value then converted into pulse width modulation to control the speed of the brusless motor, and its used to do obstacles avoidance manouver. This method might reduce overshoot on the system and make response time (rise time) arrived faster than other methods. The obstacle avoidance system requires small overshoot value and an appropriate response time to avoid frictions or collisions. The result of this research is the rise time to avoid obstacles that reached 4,7 second with flight speed of 0,6 m/s and turns for roll angle equal to 14,27 °, pitch equal to 13,26 °, and yaw equal to 9,87 ° while avoidance maneuvering obstacles.