Robust inertial navigation system/ultra wide band integrated indoor quadrotor localization employing adaptive interacting multiple model-unbiased finite impulse response/Kalman filter estimator

Xu, Yuan; Shmaliy, Yuriy S.; Chen, Xiyuan; Li, Yueyang; Ma, Wanfeng

doi:10.1016/j.ast.2020.105683

Cited by 30 publications

(6 citation statements)

References 40 publications

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“…This way, the drone flies inside the volume of a cube bounded by the position of the anchors. A similar set-up is used in [16], where the eight anchors on the corners optimise the possibility for the drone to have line-of-sight at least with one anchor. Such a grid was set-up at the Cyberzoo (shown in Figure 1), the flight arena of the TU Delft Faculty of Aerospace, which is known as the main facility of the university for performing tests on drones, and has a size of 10m × 10m × 7m.…”

Section: Methodsmentioning

confidence: 99%

Position Controller for a Flapping-Wing Drone Using UWB

et al. 2022

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This paper proposes an integral approach for accurate ultra-wideband indoor position control of flapping-wing micro-air vehicles. Three aspects are considered to achieve a reliable and accurate position controller. The first aspect is a velocity/attitude flapping-wing model for drag compensation. The model is compared with real flight data and shown to be applicable for more than one type of flapping-wing drone. The second improvement regards a voltage-dependent thrust control. Lastly, a characterisation of ground effects in flapping-wing flight is obtained from hovering experiments. The proposed controller improves position control by a factor [Formula: see text], reaching a mean absolute error of 10[Formula: see text]cm for the position in [Formula: see text] and [Formula: see text], and 4.9[Formula: see text]cm for the position in [Formula: see text].

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Section: Methodsmentioning

confidence: 99%

Position Controller for a Flapping-Wing Drone Using UWB

et al. 2022

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“…For improving the positioning accuracy, the Ultra-Wide Band (UWB) technology was proposed, which can provide decimeter-level positioning accuracy. For example, a UWB localization system was deigned for the underground coal mine application (Li et al, 2020) and the indoor quadrotor localization and so on (Xu et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

A FIR filter assisted with the predictive model and ELM integrated for UWB-based quadrotor aircraft localization

Dong

et al. 2023

Satell Navig

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To improve the accuracy of the Ultra-Wide Band (UWB) based quadrotor aircraft localization, a Finite Impulse Response (FIR) filter aided with an integration of the predictive model and Extreme Learning Machine (ELM) is proposed in this work. The FIR filter estimates the quad-rotor aircraft ’s position by fusing the positions measured with the UWB and Inertial Navigation System respectively. When the UWB dada are unavailable, both the ELM and the predictive model are used to provide the measurements, replacing those unavailable UWB data, for the FIR filter. The ELM estimates the measurement via the mapping between the one step prediction of state vector and the measurement built when the UWB data are available. For the predictive model, we mathematically describe the missing UWB data. Then, both the measurements estimated with the ELM and predictive model are employed to estimate the observations via Mahalanobis distance. The test results show that the FIR filter aided by the predictive model/ELM integrated can reduce the Cumulative Distribution Function and position Root Mean Square Error effectively when the UWB is unavailable. Compared with the ELM assisted FIR filter, the proposed FIR filter can reduce the localization error by about 48.59 %, meanwhile, the integrated method is close to the method with a better solution. Graphical Abstract

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“…In Reference 19, a tracking control with disturbances rejection was approached. Reference 20 developed an adaptive multi models‐based Kalman filter (KF) and in Reference 21, an extended Kalman filter (EKF) combined with Rauch Tung Striebel (RTS) smoother to generate accurate flight data. Reference 12 developed and demonstrated an enhanced combination of Madgwick data fuse filter that was proposed initially in References 22 and 23, with an EKF ensuring higher performance with faster convergence.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Bouaiss

Mechgoug

Taleb‐Ahmed

et al. 2023

Adaptive Control & Signal

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SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

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Robust inertial navigation system/ultra wide band integrated indoor quadrotor localization employing adaptive interacting multiple model-unbiased finite impulse response/Kalman filter estimator

Cited by 30 publications

References 40 publications

Position Controller for a Flapping-Wing Drone Using UWB

Position Controller for a Flapping-Wing Drone Using UWB

A FIR filter assisted with the predictive model and ELM integrated for UWB-based quadrotor aircraft localization

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

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