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.

翻译：本文提出一种适用于全球导航卫星系统拒止且非惯性环境下四旋翼飞行器的鲁棒控制与估计框架，在此类环境中，平台诱导加速度会导致惯性测量单元等惯性传感器失效。传统估计器在此类场景下无法区分测量加速度源自飞行器自身还是非惯性平台，从而导致状态漂移与控制性能退化。与严重依赖惯性测量单元和全球导航卫星系统的传统方法不同，本方法仅依赖外部位置测量，并结合带未知输入的扩展卡尔曼滤波器以补偿平台运动。该估计器与级联PID控制器配合实现完整的三维轨迹跟踪。为消除定位误差对估计性能的影响，所有实验均采用高精度运动捕捉系统完成。在移动推车测试平台上进行的实验验证了本方法在X轴与Y轴平移扰动下的有效性。相较于标准扩展卡尔曼滤波器，所提方法在无需惯性反馈的情况下显著提升了系统稳定性与轨迹跟踪精度，为卡车、电梯等运动平台的实际部署提供了可行方案。