A Moving Target Tracking Control and Obstacle Avoidance of Quadrotor UAV Based on Sliding Mode Control Using Artificial Potential Field and RBF Neural Networks

Chen, Xuan; Xue, Wentao; Qiu, Haiyang; Ye, Hui

doi:10.23919/ccc50068.2020.9189346

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Reference [26] effectively prevented the collision of an aircraft with obstacles through a combination of the H∞ controller and APF. Reference [27] proposed an improved artificial potential field (IAPF) based on traditional APF and sliding mode control theory, which enables UAVs to safely avoid obstacles while tracking moving targets. Reference [28] successfully achieved the goal of obstacle avoidance through the combination of a virtual structure and APF.…”

Section: Introductionmentioning

confidence: 99%

Research on Distributed Real-Time Formation Tracking Control of High-Order Multi-UAV System

2022

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To address the problem of real-time formation tracking control of multi-UAV systems, a highorder distributed real-time formation tracking control protocol is designed based on previous research. Firstly, the dynamic models of the third-order formation motion control subsystem and second-order formation attitude control subsystem are established based on the mathematical model with autopilot, and the algebraic graph theory is introduced. Secondly, considering the influence of the mass of the obstacle on the formation, the mass of the obstacle was introduced into the repulsive potential field function to improve the obstacle avoidance efficiency of the UAV. Then, according to the state information between the leader and follower of the UAV, as well as between the follower and its neighbors, a third-order real-time formation tracking control protocol is designed combined with the potential field and consensus theory. Finally, the stability is proved using the Lyapunov theory. The 3D simulation results show that the formation-tracking control accuracy and obstacle avoidance efficiency are improved by this control protocol.INDEX TERMS Consensus theory, formation tracking control, third-order integrator, the repulsive potential field function.

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Section: Introductionmentioning

confidence: 99%

Research on Distributed Real-Time Formation Tracking Control of High-Order Multi-UAV System

2022

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“…Araar et al [9] proposed a PID method with an EKF filter to track autonomously and land on a moving platform. Chen et al [10] presented sliding mode control methods based on artificial potential field and RBF neural network for a quadrotor to track the moving target. Based on studies aforesaid, the design of a controller with high precision and strong robustness is the precondition of accurate tracking.…”

Section: Introductionmentioning

confidence: 99%

A Moving Target Tracking Control of Quadrotor UAV Based on Passive Control and Super-Twisting Sliding Mode Control

Xue

Zhu

Yang

et al. 2021

Mathematical Problems in Engineering

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A novel asymptotic tracking controller for an underactuated quadrotor unmanned aerial vehicle (UAV) is proposed to solve a moving target tracking problem. Firstly, the control system is decoupled into the position control system and the attitude control system. Secondly, a method combined artificial potential field with passivity control (APF&PC) is introduced for the positioning system to achieve high-precision tracking of moving target at a fixed distance. Thirdly, a super-twisting sliding mode (STSM) method with an improved reaching law for the attitude system is applied to ensure that the attitude converges to the desired value. Furthermore, the stabilities of two subsystems are proved, and sufficient stability conditions are derived based on the passive method and Lyapunov method, respectively. Finally, simulation results of the moving target tracking verify the superiority and robustness of the proposed control method in the presence of parameter uncertainties and external disturbances.

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Event‐triggered obstacle avoidance tracking control of quadrotor unmanned aerial vehicle

Wang,

Wu,

Tang

et al. 2024

Adaptive Control & Signal

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SummaryThis article addresses the safe tracking control problem of quadrotor unmanned aerial vehicles (QUAV) considering uncertain aerodynamics and unknown external disturbances. First, a robust adaptive backstepping controller is designed, where the bias of the control variable, that is, the tracking error of the attitude subsystem, is considered in the design process of the position subsystem. Meanwhile, the safe obstacle avoidance objective is realized by the quadratic programming (QP) method that combines the tracking controller with the exponential control barrier function (ECBF). Furthermore, an event‐triggered mechanism is used for the QP process to conserve computing resources. Simulation validates the feasibility and superiority of the proposed control scheme in presence of multi‐obstacle.

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A Moving Target Tracking Control and Obstacle Avoidance of Quadrotor UAV Based on Sliding Mode Control Using Artificial Potential Field and RBF Neural Networks

Cited by 6 publications

References 19 publications

Research on Distributed Real-Time Formation Tracking Control of High-Order Multi-UAV System

Research on Distributed Real-Time Formation Tracking Control of High-Order Multi-UAV System

A Moving Target Tracking Control of Quadrotor UAV Based on Passive Control and Super-Twisting Sliding Mode Control

Event‐triggered obstacle avoidance tracking control of quadrotor unmanned aerial vehicle

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