车辆-mounted 航空系统在GPS拒止、非惯性环境中的控制与状态估计
Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
AI总结：
本文提出了一种无需惯性反馈的四旋翼飞行器控制与状态估计方法，通过外部位置测量和EKF-UI结合cascaded PID控制器，实现非惯性环境下的稳定轨迹跟踪。
摘要：
我们提出了一种鲁棒的控制和估计框架，用于在GPS拒止、非惯性环境中运行的四旋翼飞行器。在这种环境下，惯性传感器如惯性测量单元（IMUs）由于平台引起的加速度变得不可靠。在这样的设置中，传统估计器无法区分测量的加速度是来自四旋翼本身还是非惯性平台，导致漂移和控制退化。与传统方法依赖IMU和GNSS不同，我们的方法仅依赖于外部位置测量结合扩展卡尔曼滤波器与未知输入（EKF-UI）来考虑平台运动。估计器与 cascaded PID 控制器配对，实现全三维跟踪。为了将估计器性能与定位误差隔离，所有测试均使用高精度运动捕捉系统进行。在移动车测试台上进行的实验结果验证了我们的方法在X轴和Y轴平移不一致情况下的有效性。与标准EKF相比，所提出的方法显著提高了稳定性和轨迹跟踪性能，无需惯性反馈，使在如卡车或电梯等移动平台上部署成为可能。
英文摘要：
We present a robust control and estimation framework for quadrotors operating in Global Navigation Satellite System(GNSS)-denied, non-inertial environments where inertial sensors such as Inertial Measurement Units (IMUs) become unreliable due to platform-induced accelerations. In such settings, conventional estimators fail to distinguish whether the measured accelerations arise from the quadrotor itself or from the non-inertial platform, leading to drift and control degradation. Unlike conventional approaches that depend heavily on IMU and GNSS, our method relies exclusively on external position measurements combined with a Extended Kalman Filter with Unknown Inputs (EKF-UI) to account for platform motion. The estimator is paired with a cascaded PID controller for full 3D tracking. To isolate estimator performance from localization errors, all tests are conducted using high-precision motion capture systems. Experimental results in a moving-cart testbed validate our approach under both translational in X-axis and Y-axis dissonance. Compared to standard EKF, the proposed method significantly improves stability and trajectory tracking without requiring inertial feedback, enabling practical deployment on moving platforms such as trucks or elevators.
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