Backstepping Control of Drone

Saibi, Ali; Boushaki, Razika Zammoum; Belaidi, Hadjira

doi:10.3390/engproc2022014004

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Quadrotor's movement may be split into 2 control systems: a rotating sub-system (roll, pitch, and yaw) and a translating sub-system (x, y, and z coordinates). The revolving sub-system is completely actuated whilst translating sub-system is under-actuated [37]. Newton Euler equation can be formulated by (3):…”

Section: Quadrotor Dynamicsmentioning

confidence: 99%

Enhanced backstepping control for disturbances rejection in quadrotors

et al. 2022

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This work studies the issue of quadrotor trajectory tracking control in presence of disturbances and model uncertainties. The paper starts by extracting the kinematics and dynamics models of the quadrotor. This results in the motion equations, which eventually serve as a blueprint for creating the suggested smart flight control scheme. Secondly, an enhanced backstepping controller (BSC) is developed and tested to keep the quadrotor tracking the desired trajectory both in steady state and in presence of disturbances. Finally, BSC beside two other controllers: sliding mode controller (SMC) and proportional derivative controller (PDC) are implemented in MATLAB/Simulink and the obtained results are compared and conclusions are extracted. Therefore, it is established that PDC is not robust to disturbances as noise will be amplified due to the derivative term. Whereas, although SMC is robust to parameter variations and disturbances; however, it is not continuous which may affect the actuators due to the increased gains which may saturate them. In contrast, BSC requires too many tuning parameters; however, it ensures Lyapunov Stability and does not depend on the system as it does not involve cancelling system nonlinearity. Moreover, BSC results are 1017 better than the results of the two other controllers.

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Section: Quadrotor Dynamicsmentioning

confidence: 99%

Enhanced backstepping control for disturbances rejection in quadrotors

et al. 2022

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“…The authors in [7], [9], [10], [11] derived the quadcopter mathematical model and employed a linearized optimal Proportional-Integral-Derivative (PID) controller to track the desired trajectory, while both of the authors in [12], [13] used a Linear Quadratic Regulator (LQR) controller. The authors in [14], [15], [16], [17] utilized the optimal nonlinear Backstepping Controller (BSC), and the authors in [18], [19], [20]proposed an optimal Integral Backstepping Controller (IBSC) to control the attitude and altitude of the quadcopter. The authors in [21], [22], [23] suggested the Sliding Mode Controller (SMC), while the authors in [24], [25], [26] proposed the Integral Sliding Mode Controller (ISMC) for tracking the desired trajectory with high robustness.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Tracking: A Comparative Study of Autonomous Quadcopter Controllers

2024

jjmie

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The quadcopter controller plays a significant role in autonomous vehicle trajectory tracking and has been extensively examined in the literature because it is a highly nonlinear and under-actuated system. This article contributes by methodically comparing PID, BSC, IBSC, SMC, and ISMC control algorithms under standardized conditions. The study involves implementing these algorithms and optimizing their parameters using a hybrid Flower Pollination Algorithm -Genetic Algorithm (FPA-GA). Robust testing scenarios, including mass uncertainty and time-varying disturbances, are employed to assess algorithmic performance. The research centers on trajectory tracking-control approaches for highly maneuverable Unmanned Aerial Vehicles (UAVs) equipped with four heave thrusters, emphasizing controller stability through Lyapunov functions. Theoretical exploration precedes comprehensive numerical simulations, enabling a detailed comparison of the efficacy and resilience of the proposed approaches. Results from exhaustive testing reveal the consistent superiority of the BSC controller, demonstrating notably low Integral of Time-weighted Squared Error (ITSE) values of 0.1253 and 0.5262 from two simulation tests. These findings underscore the practical advantages of the BSC controller in highly dynamic UAV trajectory tracking scenarios.

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“…At present, the widely used quad-rotor helicopter attitude control methods mainly include PID control [13][14][15], sliding mode control [16][17][18], backstepping control [19][20][21], neural network control [22,23], active disturbance rejection control (ADRC) [24][25][26][27] and so on. PID control is composed of proportional, integral and differential parts.…”

Section: Introductionmentioning

confidence: 99%

A Steady-State Flight Control Algorithm Combining Stretching Ratio Coefficient and PID Control for UAVs in Uncertain Environments

Wang¹,

Tan²,

Nie³

2022

Sustainability

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Unmanned aerial vehicle (UAV) has the advantages of flexible operation, simple structure, and low price, which has been widely researched. In recent years, the application of small UAVs has become more extensive, and the steady-state control of UAVs has great research prospects and value due to it being the key to better execute flight task. A PID steady-state control algorithm based on color recognition and target detection is designed herein. Firstly, it is necessary to calculate the distance between the coordinates of the center of the UAV screen and the geometric center of the target point. Secondly, a pixel distance correction algorithm based on actual distance is proposed so as to correct pixel distance deviation. Finally, it is necessary to control the speed of the UAV by a PID control algorithm to achieve the goal that the UAV is stable near the geometric center of the target point. In short, this algorithm realizes the functions of real-time video transmission of the UAV, flight data storage, color recognition, and speed control of the UAV based on the PID control algorithm and distance correction. The experimental results demonstrate that the proposed algorithm has good robustness, makes the UAV have better stability, and can be used for the process of target tracking in uncertain environments.

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Backstepping Control of Drone

Cited by 6 publications

References 17 publications

Enhanced backstepping control for disturbances rejection in quadrotors

Enhanced backstepping control for disturbances rejection in quadrotors

Optimal Trajectory Tracking: A Comparative Study of Autonomous Quadcopter Controllers

A Steady-State Flight Control Algorithm Combining Stretching Ratio Coefficient and PID Control for UAVs in Uncertain Environments

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