Gain-Scheduling and PID Control for an Autonomous Aerial Vehicle with a Robotic Arm

2018 ◽  
Author(s):  
Aaron Lopez Luna ◽  
Israel Cruz Vega ◽  
Jose Martinez Carranza
Keyword(s):  
Pid Control ◽  
Gain Scheduling ◽  
Robotic Arm ◽  
Aerial Vehicle

scholarly journals PID Control System on Brushless DC Motor for Quadcopter Balance

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 110-114
Author(s):  
Mochamad Mobed Bachtiar ◽  
Fernando Ardilla ◽  
Muhammad Faiz Hasbi ◽  
Iwan Kurnianto Wibowo
Keyword(s):  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Unmanned Aircraft ◽  
Integrated System ◽  
Brushless Dc Motor ◽  
Test Results ◽  
Brushless Dc ◽  
Aerial Vehicle ◽  
Aircraft System

Unmanned Aerial Vehicle (UAV) is an unmanned aircraft system that is no longer a special need but has become a general need for the community, and one example is used to capture everyday moments through photos or videos from the air. Among the models of UAV aircraft is the quadcopter, where there is a flight controller that functions to fly the quadcopter by adjusting the speed of each motor. The flight controller that is often used today is the Pixhawk manufacturer. The Pixhawk module is an integrated system that the factory has provided, so it cannot be modified in terms of control and I/O. This research focuses on making an independent flight controller that can be used to fly a quadcopter. The control method that is implanted is Proportional Integral Derivative or commonly known as PID. The flight controller uses the PID control method to adjust each Brushless DC Motor (BLDC) speed to maintain stability while flying. From the test results, the quadcopter can fly stably with KP parameters of 2.5, KI of 0.6, and KD of 1.0. The response time in processing feedback is 3s.

Modelling and PID Control of a Quadrotor Aerial Robot

2014 ◽  
Vol 903 ◽  
pp. 327-331 ◽  
Author(s):  
Ismail Mohd Khairuddin ◽  
Anwar P.P.A. Majeed ◽  
Ann Lim ◽  
Jessnor Arif M. Jizat ◽  
Abdul Aziz Jaafar
Keyword(s):  
Pid Control ◽  
Pitch Angle ◽  
Pid Controller ◽  
Pd Controller ◽  
Proportional Integral Derivative ◽  
Hovering Flight ◽  
Proportional Integral ◽  
Aircraft Model ◽  
Aerial Robot ◽  
Aerial Vehicle

This paper elucidates the modeling of a + quadrotor configuration aerial vehicle and the design of its attitude and altitude controllers. The aircraft model consists of four fixed pitch angle propeller, each driven by an electric DC motor. The hovering flight of the quadrotor is governed by the Newton-Euler formulation. The attitude and altitude controls of the aircraft were regulated using heuristically tuned (Proportional-Integral-Derivative) PID controller. It was numerically simulated via Simulink that a PID controller was sufficient to bring the aircraft to the required altitude whereas the attitude of the vehicle is adequately controlled by a PD controller.

Dynamic Inversion and Gain-Scheduling Control for an Autonomous Aerial Vehicle with Multiple Flight Stages

2018 ◽  
Vol 29 (3) ◽  
pp. 328-339 ◽  
Author(s):  
Natassya B. F. Silva ◽  
João V. C. Fontes ◽  
Roberto S. Inoue ◽  
Kalinka R. L. J. C. Branco
Keyword(s):  
Gain Scheduling ◽  
Dynamic Inversion ◽  
Aerial Vehicle ◽  
Gain Scheduling Control

scholarly journals Fuzzy Gain Scheduling PID Control for Position of the AR.Drone

2018 ◽  
Vol 8 (4) ◽  
pp. 1939 ◽  
Author(s):  
Agung Prayitno ◽  
Veronica Indrawati ◽  
Ivan Immanuel Trusulaw
Keyword(s):  
Fuzzy Logic ◽  
Pid Control ◽  
Gain Scheduling ◽  
Logic Model ◽  
Pid Controllers ◽  
Process Scheduling ◽  
Tuning Parameters ◽  
Control Scheme ◽  
Integral Error ◽  
Takagi Sugeno

This paper describes the design and implementation of fuzzy gain scheduling PID control for position of the AR.Drone. This control scheme uses 3 PID controllers as the main controller of the AR.Drone, in this case to control pitch, roll and throttle. The process of tuning parameters for each PID is done automatically by scheduling determined by Takagi-Sugeno-Kang (TSK) fuzzy logic model. This paper uses five function sets of PID parameters that will be evaluated by fuzzy logic in order to tune PID controllers. Error position (x,y,z), as inputs of controller, enters the PID Signal block yielding the ouputs in term of error, integral error and differential error. These signal become the inputs of the fuzzy scheduler to yield outputs pitch, roll and throttle to the AR.drone. The control scheme is implemented on the AR.Drone to make it fly to forming a square in the room. The experimental results show that the control scheme can follow the desired points, and process scheduling PID parameters can be shown.

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