Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control

Cai, Zhihao; Lou, Jiang; Zhao, Jiang; Wu, Kun; Liu, Ningjun; Wang, Ying Xun

doi:10.1016/j.ymssp.2019.03.035

Cited by 74 publications

(35 citation statements)

References 18 publications

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“…where k 1 and k 2 are the feedback gain constant, f (•) ∈ R 4 is a delay kernel function satisfies f (0) = 0 and f (0) = 0, φ * is an expected heading angle,φ i (t) = ω i (t) is the heading angular velocity change rate of the i-th UAV, u i and v i are the corresponding followers' control input. Then, from equations (6) and (8), we havë…”

Section: B Modelmentioning

confidence: 99%

“…In civil applications, it has been widely used in agriculture, services and other areas such as spraying pesticides, geological exploration, archaeological exploration, logistics and transportation [2], [3]. Regardless of military career or civilian market, the breakthrough in technical of UAV means a significant increase in work efficiency [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

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UAVs’ Formation Keeping Control Based on Multi–Agent System Consensus

2020

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In this paper, formation keeping control of unmanned aerial vehicles (UAVs) based on multi-agent system consensus is studied. Firstly, a leader-following model based multiple unmanned aerial vehicles' (multi-UAVs') formation system is proposed. In which, every UAV has a heading keep autopilot as standard flight controller, and the heading control signal is transmitted by a nonlinear feedback controller with time delays. Secondly, some criteria of stability and Hopf bifurcation conditions for the equilibrium point of the leader UAV are established by using the Routh-Hurwitz criterion. Then, the consensus protocol is designed. A model prediction controller is introduced to make followers predict the leader's status and maintain a relative position in the formation, and eventually reach a consensus with the leader. Finally, some simulation examples are given to verify the correctness of the conclusions.

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Section: B Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAVs’ Formation Keeping Control Based on Multi–Agent System Consensus

2020

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“…Based on Cai et al (2019), the CGWO is proposed to optimize the parameters in cp . The structure of this process is given in Figure 3.…”

Section: Trajectory Tracking and Obstacle Avoidancementioning

confidence: 99%

“…A virtual target guidance approach is proposed for quadrotor obstacle avoidance based on Cai et al (2019). A typical obstacle avoidance scenario by using proposed virtual target guidance approach is shown in Figure 5.…”

Section: Trajectory Tracking and Obstacle Avoidancementioning

confidence: 99%

Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer

Wang

Cai

et al. 2020

Transactions of the Institute of Measurement and Control

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In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor trajectory tracking and obstacle avoidance. Comparative simulations and Monte Carlo tests are carried out to demonstrate the effectiveness and robustness of the CGWO-based backstepping control scheme and virtual target guidance approach.

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“…However, these linear approaches are sensitive to the nonlinearities, and the flight performance will be degraded when the disturbances occur. In order to improve the robust control performance, more efforts have been made on the nonlinear control approaches, such as backstepping control [17], sliding mode control (SMC) [18], active disturbance rejection control (ADRC) [19], fuzzy control and other intelligent control methods [20]- [22]. The SMC method is insensitive to the disturbances and is widely applied for the nonlinear systems [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Robust and Adaptive Backstepping Control for Hexacopter UAVs

Zhang

Deng

et al. 2019

IEEE Access

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A nonlinear robust and adaptive backstepping control strategy is hierarchically proposed to solve the trajectory tracking problem of hexacopter UAVs. Due to the under-actuated and coupled properties of the hexacopter dynamics, the nominal backstepping control approach is fully designed as the main controller. Considering the model uncertainties and external disturbances perturbing the system stability, a robust 2 nd-order linear extended state observer (LESO) with more reliable velocity feedback is devised to observe and suppress the instabilities, and peaking phenomena in the observation are removed. Usually, large observer gains are selected to reduce the tracking errors but will amplify the measurement noise. To further enhance the system robustness, an adaptive switching function based compensator is introduced to eliminate the observation errors, through which the requirement on large observer gains is relaxed, and high gain behaviors of the LESO are avoided. Stability analysis proves that the nonlinear control scheme can ensure the hexacopter UAV asymptotic tracking along the designated trajectory. Comparative simulations under different controllers are carried out to demonstrate the efficiency and superiority of the proposed control scheme. INDEX TERMS Hexacopter UAV, trajectory tracking, robust backstepping control, 2 nd-order LESO, hierarchical compensators.

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Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control

Cited by 74 publications

References 18 publications

UAVs’ Formation Keeping Control Based on Multi–Agent System Consensus

UAVs’ Formation Keeping Control Based on Multi–Agent System Consensus

Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer

Robust and Adaptive Backstepping Control for Hexacopter UAVs

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