An Autonomous Control of Fuzzy-PD Controller for Quadcopter

Self Cite

In this paper, a design of a fuzzy-PID controller for path tracking of a mobile robot with differential drive is proposed. The fuzzy-PID controller consists of a PID controller and a fuzzy controller with two inputs and three outputs. When the system response has the error and the error rate, the fuzzy controller can tune the parameters of the PID controller. The model based on Lagrange dynamic approach for a robot with differential drive is described. The fuzzy-PID controller and the classical PID controller are compared by simulation. The path tracking of a mobile robot with differential drive was tested using MATLAB/Simulink. The simulation results show that the fuzzy-PID controller has a better performance than the classical PID controller. The proposed controller has better convergence rate in comparison with the classical PID controller for a mobile robot with any arbitrary initial state. It has the advantages of rapid respond, high stability, tracking accuracy and good anti-interference, so the fuzzy-PID controller is the appropriate choice for path tracking control of mobile robots with differential drive

Section: Simulationmentioning

confidence: 92%

“…To determine the control, the parameters use the PID gains online [10,14]. The modified controller gains are given by linear transformation as follows:…”

Section: Design Of Controllermentioning

confidence: 99%

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Design of Fuzzy-PID Controller for Path Tracking of Mobile Robot with Differential Drive

Tiep¹,

Lee²,

Im³

et al. 2018

Self Cite

“…In [15], the drone velocities were estimated using frame registration. In [16] and [17], the fuzzy controllers were studied for quadcopter trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Nam¹,

Yeom²

2020

Small unmanned aerial vehicles can be effectively used for aerial video surveillance. Although the field of view of the camera mounted on the drone is limited, flying drones can expand their surveillance coverage. In this paper, we address the detection of moving targets in urban environments with a moving drone. The drone moves at a constant velocity and captures video clips of moving vehicles such as cars, buses, and bicycles. Moving vehicle detection consists of frame registration and subtraction followed by thresholding, morphological operations and false blob reduction. First, two consecutive frames are registered; the coordinates of the next frame are compensated by a displacement vector that minimizes the sum of absolute difference between the two frames. Second, the next compensated frame is subtracted from the current frame, and the binary image is generated by thresholding. Finally, morphological operations and false alarm removal extract the target blobs. In the experiments, the drone flies at a constant speed of 5.1 m/s at an altitude of 150 m while capturing video clips of nine moving targets. The detection and false alarm rates as well as the receiver operating characteristic curves are obtained, and the drone velocities in the x and y directions are estimated by the displacement vector. The average detection rate ranges from 90% to 97% while the false alarm rate ranges from 0.06 to 0.5. The root mean square error of the speed is 0.07 m/s when the reference frame is fixed, showing the robustness of the proposed method.

“…Other researchers used a method of applying an oscillatory vertical force to the pendulum pivot [7]. [8,9] proposed a FLC to control the rotary of an IP.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Position Control of a Gyroscopic Inverted Pendulum Using PID, Fuzzy Logic and Fuzzy PID controllers

Rabah¹,

Rohan²,

Kim³

2018

This paper presents the modeling and control of a gyroscopic inverted pendulum. Inverted pendulum is used to realize many classical control problems. Its dynamics are similar to many real-world systems like missile launchers, human walking and many more. The control of this system is challenging as it's highly unstable which will tend to fall on either side due to the gravitational force. In this paper, we are going to stabilize the gyroscopic inverted pendulum using PID controller, fuzzy logic controller and fuzzy PID controller. These controllers will be compared to determine the performance and to distinguish which one has the better response. Experiments and simulations are conducted to examine the different controllers and results are presented under different disturbance scenarios.