Abstract
Autonomous estimation of the state is of key importance in UAVs, as the measurement systems may experience faults and failures. Thus estimation techniques must provide estimates of the most important variables used in the control algorithms for safe, autonomous, unmanned flights. In this paper, a filter with low computational complexity for attitude estimation of a quadrotor UAV is introduced, with a model suitable for Fault-Tolerant Observation. The new filtration method, called the Square Root Unscented Complementary Kalman Filter (SRUCKF), is based on the commonly-known Kalman Filter (KF) in its nonlinear version, namely the Square Root Unscented Kalman Filter (SRUKF). The fundamental equation of the KF is modified so that the complementary feature of the filter is exalted. The new filter introduces characteristics that are analyzed on the basis of its application in quadrotor state estimation. Finally, the results are compared to an ordinary filter of the same type (using the Unscented Transformation). The presented studies indicate that the newly derived filter (SRUCKF) handles strong nonlinearities and gives results similar to those obtained from the SRUKF. Furthermore, it introduces lower computational burden, as the undergoing process uses diagonal matrices in its crucial places. In the paper, the estimation algorithms are tailored to a quadrotor UAV (Crazyflie 2.0), for which a quaternion-based model is proposed. The contribution of the paper lies in a Kalman-based novel state observer and its application in Fault-Tolerant Observation (FTO).
Highlights
Unmanned aerial vehicles have been an area of development
The Extended Kalman Filter (EKF) works on a basis of the Kalman Filter theory, with the difference that in each step the nonlinear representation is linearized and the Jacobian matrix is used as the system matrix
By making a fusion of the AHRS data and the model state, the robot state can be observed. This simple idea was tested in attitude estimation with the Unscented Kalman Filter in [21] and with the Adaptive Square-Root Unscented Kalman Filter (ASRUKF)
Summary
Unmanned aerial vehicles have been an area of development. These flying vehicles have a wide range of applications and offer tremendous potential. By making a fusion of the AHRS data and the model state, the robot state can be observed This simple idea was tested in attitude estimation with the Unscented Kalman Filter in [21] and with the Adaptive Square-Root Unscented Kalman Filter (ASRUKF). The most important, practical/useful advantage of the approach proposed here, in relation to the solutions available in commercial mid-range price quadrotors (like the popular PixHawk controller or XSens AHRS unit with stand-alone state estimation solutions already implemented in their firmware), is the openness and flexibility of the solution when the drone has to perform an unconventional task in very difficult conditions under variable load. Due to the multitude of issues and the potential volume of work, the authors decided to divide the results into a series of four articles dedicated to: UAV dynamics modeling [28], effective estimation methods, fault-tolerant estimation and control, as well as fusion of measurement and video data. In the last section conclusions are drawn and a summary is given
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