Computationally Efficient Attitude Estimation with Extended H2 Filtering

Kim, Sunsoo; Tadiparthi, Vaishnav; Bhattacharya, Raktim

doi:10.2514/1.g005140

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…REMARK 1 The sensing error ỹ(ρ) 0 , caused by noise, sensor dynamics, and sensing bias, is typically bounded [30]. When the signal ŷ(ρ) 0 is generated by an estimator or a filter, the boundedness of the estimation error is a design requirement [12], [31].…”

Section: A Derivation Of the Indi Controlmentioning

confidence: 99%

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Adaptive Nonlinear Incremental Flight Control for Systems With Unknown Control Effectiveness

Chang

Breuker

Wang

2023

IEEE Trans. Aerosp. Electron. Syst.

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This article exposes that although some sensor-based nonlinear fault-tolerant control frameworks including incremental nonlinear dynamic inversion control can passively resist a wide range of actuator faults and structural damage without requiring an accurate model of the dynamic system, their stability heavily relies on a sufficient condition, which is unfortunately violated if the control direction is unknown. Consequently, it is proved in this article that no matter, which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented, none of the existing nonlinear incremental control methods can guarantee closed-loop stability. Therefore, this article proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially known (control direction is unknown) or even completely unknown control effectiveness. Its effectiveness is proved in the Lyapunov sense and is also verified via numerical simulations of an aircraft attitude tracking problem in the presence of sensing errors, parametric model uncertainties, structural damage, actuator faults, as well as inversed and unknown control effectiveness.

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Section: A Derivation Of the Indi Controlmentioning

confidence: 99%

“…, |w m |}, then as long as any one of the signs of w i is incorrectly estimated, the stability criterion (15) in Theorem 1 will be broken. PROOF For the system in (31), the stability condition (15) becomes…”

Section: B Stability Analysis For Unknown Control Directionsmentioning

confidence: 99%

Adaptive Nonlinear Incremental Flight Control for Systems With Unknown Control Effectiveness

Chang

Breuker

Wang

2023

IEEE Trans. Aerosp. Electron. Syst.

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“…Figure 5 illustrates the computation of the UAV position at time n. The distribution shows the joint posterior density of the UAV position q at any time n, and the landmark positions l, based on the observations and motion from time 0 to n with respect to the initial position q 0 [142]. To fix misalignment issues that may come up with the imagery data, the point clouds may be colorized to enhance the level of detail and accuracy of image sensors [156]. In order to create a point cloud with static scanning accuracy ±x millimeters, a UAV needs to reset the sensor scanner and post-process the data into an exportable point cloud [125].…”

Section: Visual Odometry and Photogrammetry In Uavmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta

Fernando

2022

Drones

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This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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“…e attitude measurement system is used to determine the attitude of the UAV at any time, and it is a prerequisite for the control, management, and use of the UAV. At present, the equipment that measured the attitude of the UAV usually includes inertial navigation system (INS), vision navigation system (VisNav), Global Positioning System (GPS), infrared sensors, global navigation satellite system (GNSS), and BeiDou Navigation Satellite System (BDS) [32][33][34][35]. According to the number of measurement equipment, the attitude measurement system can be divided into the single-equipment measurement system and the multiequipment measurement system [36,37].…”

Section: Attitude Measurement Model Of Uavmentioning

confidence: 99%

EKF-AF PID-Based Attitude Control Algorithm for UAVs

Song

Jiang

et al. 2022

Mobile Information Systems

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Aiming at the problems of manual setting of control rules and low control accuracy of the fuzzy PID control method, an attitude control method of the unmanned aerial vehicle (UAV) based on the extended Kalman filter (EKF) and adaptive fuzzy PID (AF PID) is proposed. The dynamics equations and measurement equations of UAV are established, and the EKF is used to estimate and predict attitude changes; an adaptive fuzzy PID control algorithm is designed, and the adaptive adjustment method is adopted to revise fuzzy control rules and parameters online; a simulation platform of the attitude control system (ACS) of UAV is built to simulate and verify the control effect of the method. The simulation results show that the proposed algorithm can estimate the change of attitude angles accurately and improve the control accuracy; meanwhile, it also ensures the stability and rapidity of the attitude changes. The research results can resolve the contradiction between high precision and rapid stability of complex systems to a certain extent.

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Computationally Efficient Attitude Estimation with Extended H2 Filtering

Cited by 6 publications

References 11 publications

Adaptive Nonlinear Incremental Flight Control for Systems With Unknown Control Effectiveness

Adaptive Nonlinear Incremental Flight Control for Systems With Unknown Control Effectiveness

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

EKF-AF PID-Based Attitude Control Algorithm for UAVs

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