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<p>Currently, GPS-denied uncrewed aerial vehicle (UAV) employing image-based visual servoing (IBVS) strategies can effectively achieve target positioning and tracking. However, a critical challenge that urgently needs to be addressed is the loss of the target due to the impact of uncertain disturbances on the UAV field of view (FOV). In response to this issue, this paper proposes a dual-layer control architecture that integrates model predictive control (MPC) with an extended Kalman filter (EKF), which can effectively mitigate the negative effects caused by system noise, model errors, and external disturbances. In this framework, the EKF operates as an online observer that estimates both the quadrotor states and the disturbances in complex environments, thereby supplying the MPC with an updated model at each time step. Utilizing this refined model, the MPC computes control inputs that actively counteract the negative effects of uncertainties, thereby enhancing the control robustness. The proposed scheme enables the quadrotor to maintain a safe FOV during flight missions, ensures convergence of the state errors, and achieves stable target tracking. The paper establishes the IBVS model of the quadrotor, then designs the control scheme under various adverse disturbances. Theoretical stability analysis is provided to guarantee the algorithm stability. Finally, the effectiveness of the proposed control scheme is validated through numerical simulations and experiments.</p>

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EKF-based MPC scheme for image-based visual servoing of UAVs subject to multiple uncertain disturbances

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Guanyu Lai,
  • Bo Zhuang,
  • Yuke Ouyang,
  • Jianing Tang

摘要

Currently, GPS-denied uncrewed aerial vehicle (UAV) employing image-based visual servoing (IBVS) strategies can effectively achieve target positioning and tracking. However, a critical challenge that urgently needs to be addressed is the loss of the target due to the impact of uncertain disturbances on the UAV field of view (FOV). In response to this issue, this paper proposes a dual-layer control architecture that integrates model predictive control (MPC) with an extended Kalman filter (EKF), which can effectively mitigate the negative effects caused by system noise, model errors, and external disturbances. In this framework, the EKF operates as an online observer that estimates both the quadrotor states and the disturbances in complex environments, thereby supplying the MPC with an updated model at each time step. Utilizing this refined model, the MPC computes control inputs that actively counteract the negative effects of uncertainties, thereby enhancing the control robustness. The proposed scheme enables the quadrotor to maintain a safe FOV during flight missions, ensures convergence of the state errors, and achieves stable target tracking. The paper establishes the IBVS model of the quadrotor, then designs the control scheme under various adverse disturbances. Theoretical stability analysis is provided to guarantee the algorithm stability. Finally, the effectiveness of the proposed control scheme is validated through numerical simulations and experiments.