← 研踪 PaperTrailarXiv:2611.11555 · v1
Concurrent Learning-Based Inertial Parameter Estimation and Control of Quadrotors
首次提交 2026-11-09 02:58
版本更新 2026-11-09 02:58
雷达观测 2026-11-10 13:19
摘要
In this letter, we consider trajectory tracking control and inertial parameter estimation of a quadrotor UAV whose center of mass (CoM) does not coincide with its geometric center. Onboard payloads like an IMU, a camera, or a battery are not necessarily placed at its geometric center. Moreover, during the flight these sensors might move because of external disturbances and cause a CoM offset. Thus, these events and equipment introduce an offset and cause the inertia matrix to be non-diagonal. We develop an integral concurrent learning (ICL)-based geometric controller that estimates the mass, the CoM offset, and the full inertia matrix online, without an accelerometer or numerical derivatives, and tracks a given trajectory corrected by the offset estimated online. We prove that the estimated parameters converge to their true values under a finite excitation condition. We validate the effectiveness of the proposed algorithm using a simulation example.
作者备注：6 pages, 4 figures. To be submitted to IEEE Control Systems Letters (L-CSS) with the CDC 2027 option
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v1 · 2026-11-09 02:58 · 最新观测版本
Concurrent Learning-Based Inertial Parameter Estimation and Control of Quadrotors
In this letter, we consider trajectory tracking control and inertial parameter estimation of a quadrotor UAV whose center of mass (CoM) does not coincide with its geometric center. Onboard payloads like an IMU, a camera, or a battery are not necessarily placed at its geometric center. Moreover, during the flight these sensors might move because of external disturbances and cause a CoM offset. Thus, these events and equipment introduce an offset and cause the inertia matrix to be non-diagonal. We develop an integral concurrent learning (ICL)-based geometric controller that estimates the mass, the CoM offset, and the full inertia matrix online, without an accelerometer or numerical derivatives, and tracks a given trajectory corrected by the offset estimated online. We prove that the estimated parameters converge to their true values under a finite excitation condition. We validate the effectiveness of the proposed algorithm using a simulation example.
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