PSO Based Optimal Gain Scheduling Backstepping Flight Controller Design for a Transformable Quadrotor

Derrouaoui, Saddam Hocine; Bouzid, Yasser; Guiatni, Mohamed

doi:10.1007/s10846-021-01422-1

Cited by 26 publications

(16 citation statements)

References 46 publications

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“…This arises as a result of the plant's basic settings. The following WPs are accomplished by establishing the appropriate heading and yaw and then proceeding at a determined speed to a specified distance [1,2,[27][28][29][30].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It is an under-actuated, lively vehicle with four input militaries and six output organizes. Quadrotor, calm of four rotors with proportionally preparation where two slanting motors (1 and 2) are consecutively in the same way while the others (3 and 4) in the other way to remove the anti-torque [1][2][3][4][5].The technique used in the quadcopter system, controller design, implementation of PSO algorithm [5][6][7], and procedure for achieving the best tuning. Also, the mathematical model and analysis of PSO algorithms are explained.…”

Section: Introductionmentioning

confidence: 99%

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Quadcopter Vehicle Proportional-Integral-Derivative Controller (PIDC) Tuning by Particle-Swarm Optimization (PSO)

Gizi

2022

EAI Endorsed Trans Creat Tech

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Inspection of the faults and damages in high voltage transmission lines is not only unsafe and costly but also a very time-consuming process. In addition, it demands highly skilled manpower for the operation at tens of meters above the ground on cables carrying thousands of Volts. Meanwhile, the Quadcopter Aerial Vehicle (QAV) system became a popular alternative for aerial photography applications due to its low cost, ease of usage, and fast response wherein the proportional-integral-derivative controller (PIDC) is used. The PID controller compares the expected location of the drone with the actual measured position thereby can accurately detect the faults in the transmission lines. Based on these factors, in this study, the flight simulation is done according to the desired flight path, and drone imagery is used for (insulator, power line, and porcelain) flaw identification. Furthermore, three movements (roll, pitch, and yaw) of the quadcopter were controlled by different PIDC that were optimized using the particle swarm optimization (PSO). The MATLAB/Simulink application was used to develop the system and simulate the results (R2021b). This clearly suggested that the quadcopter continues to fly on a trajectory with minimum inaccuracy, wherein the PIDC operated as a closed-loop adaptive controller. Finally, the PSO-PIDC outperformed the PIDC due to the critical nature of manual gain modification for quadcopter flight stability.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quadcopter Vehicle Proportional-Integral-Derivative Controller (PIDC) Tuning by Particle-Swarm Optimization (PSO)

Gizi

2022

EAI Endorsed Trans Creat Tech

View full text Add to dashboard Cite

show abstract

“…Many methods have been proposed for PID gain scheduling. For instance, the authors of [ 26 ] proposed an optimal gain scheduling backstepping controller based on the Particle Swarm Optimization (PSO) algorithm. Another scheduling approach was proposed in [ 27 ], where the authors proposed a single neural adaptive PID (SNA-PID) for rotor speed control.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Melo

Andrade

Guedes

et al. 2022

Sensors

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Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.

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“…They have many advantages compared to the fixed-wing drones in terms of maneuverability, structure and versatility. Moreover, they have a simple mechanical design, a small size and a low cost [1,2,3,4,5,6,7]. Quadrotors are widely exploited in military and civilian missions such as, search and rescue, mapping, protection of sensitive points, and infrastructure inspection [8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Controller Based on the Sliding Mode Observer for a QBall 2+ Quadcopter with Experimental Validation

Daadi¹,

Boulebtinai²,

Derrouaoui³

et al. 2022

IJRCS

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This paper studies a particular Unmanned Aerial Vehicle (UAV), called QBall 2+ quadcopter. This vehicle is a complex system, non-linear, strongly coupled, and under-actuated. First, a non-linear model was developed to represent the dynamics of the studied drone. Once the latter is established, the linear model was used to obtain the best gains of the Proportional Integral Derivative (PID) controller. This controller was applied after on the non-linear model of the UAV. Moreover, a Sliding Mode Controller (SMC) based on Sliding Mode Observer (SMO) was designed for retrieving the system unknown variables. Through these latter, the QBall 2+ was controlled, taking into account the observer errors. The first contribution in this work is to implement the PID regulator on the QBall 2+ flight controller to validate the results obtained by simulation. Secondly, due to the limitations of the Flex 3 cameras, especially when the drone is outside their working environment, the sliding mode observer was implemented to replace the cameras in order to measure the states of the system considered in this work. Simulation results of the different applied controllers were displayed to evaluate their effectiveness.

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PSO Based Optimal Gain Scheduling Backstepping Flight Controller Design for a Transformable Quadrotor

Cited by 26 publications

References 46 publications

Quadcopter Vehicle Proportional-Integral-Derivative Controller (PIDC) Tuning by Particle-Swarm Optimization (PSO)

Quadcopter Vehicle Proportional-Integral-Derivative Controller (PIDC) Tuning by Particle-Swarm Optimization (PSO)

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Sliding Mode Controller Based on the Sliding Mode Observer for a QBall 2+ Quadcopter with Experimental Validation

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