LED chip accurate positioning control based on visual servo using dual rate adaptive fading Kalman filter

Wang, Ziyue; Shuili, Gong; Li, Delong; Zhou, Diyi; Lu, Huaiqing

doi:10.1016/j.isatra.2018.11.026

Cited by 14 publications

(6 citation statements)

References 25 publications

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“…Additionally, in terms of the adaptive approach, there is another popular way given by (Xia et al, 1994), which has been adopted in many studies (Loh et al, 2000; Wang et al, 2019; Haghighi and Pishkenari, 2021; Ma et al, 2021). A comparison with Xia’s method is also made for both the dual-rotor model and the rotor-bearing system.…”

Section: Discussionmentioning

confidence: 99%

“…A probable explanation goes as follows. In contrast to equation (21), the previous way (Wang et al, 2019; Xia et al, 1994) to compute λk+1 is given in equation (23). As can be seen in equation (23), the innovation here involves not only the value for the current step but also data related to previous steps, which means past data play a greater role in determining the forgetting factor compared to the approach in this paper, and this could be probably the reason for the observed divergence…”

Section: Discussionmentioning

confidence: 99%

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Unbalance identification for a practical turbofan engine using augmented Kalman filter improved with the convergence criterion

Zhou

Zhang

et al. 2023

Journal of Vibration and Control

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Kalman filter has emerged as a powerful tool for unbalance identification in rotating machinery. Recently, the augmented Kalman filter combined with the finite element model has grown up and projects its potential for complex rotor systems. This paper investigates the application of the augmented Kalman filter (AKF) to a practical turbofan engine. The current study reveals that using steady-state responses as measurements can cause fluctuation in the estimated results, even divergence for some cases, while the available signals in practice are steady-state responses generally. To the authors' knowledge, this practical problem is revealed for the first time. To address the problem, the convergence criterion is employed to improve the AKF and formulates the adaptive fading augmented Kalman filter (AFAKF) proposed in this paper. Results indicate that the increase of the amplification factor, the insufficient measurement points, and the complexity of the dynamic model can all lead to the deterioration of the estimated unbalance. The proposed AFAKF method shows favorable convergence and can achieve accurate estimation with less than 5% relative errors, and the superiority over AKF in computation cost is also observed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Unbalance identification for a practical turbofan engine using augmented Kalman filter improved with the convergence criterion

Zhou

Zhang

et al. 2023

Journal of Vibration and Control

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“…However, in practical applications, the model parameters often deviates from the real system to a certain extent, thus the normal Kalman filter cannot guarantee the filter convergence. Kalman filter adopts iterative algorithm, where the state estimation at certain instant is influenced by all observation data [33]. Adaptive fading Kalman Filter (AFKF) uses the forgetting factor to adjust the weights of all observation data in real-time, to improve the utilization of the new observation data, and to reduce the influence of old observation data to state estimation.…”

Section: A Afkf Algorithmmentioning

confidence: 99%

“…W k and V k are the sequences of state and measurement noise. The adaptive fading Kalman filter algorithm (AFKF) is given as follows [33],…”

Section: A Afkf Algorithmmentioning

confidence: 99%

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Zhang

Tang

et al. 2020

IEEE Access

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This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator. INDEX TERMS Visual tracking, robot manipulation, Kalman filter, occlusion.

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“…Moreover, dual rate high order holds were used in Solanes et al (2011) to estimate the set of visual features vectors in order to compensate for the vision delay in visual servoing mechanisms. Furthermore, a dual rate adaptive fading Kalman filter algorithm with delay compensation was presented in Wang et al (2019) to compensate for the visual information delay and achieve the accurate time sequential coordination of encoder and visual feedback in visual servoing systems. Finally, a dynamic visual tracking control system for robot manipulators was proposed in Qu et al (2020) using the dual rate adaptive fading Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Generalization of reference filtering control strategy for 2D/3D visual feedback control of industrial robot manipulators

Solanes

Muñoz-Benavent

Armesto

et al. 2021

International Journal of Computer Integrated Manufacturing

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This paper develops the application of the Dual Rate Dual Sampling Reference Filtering Control Strategy to 2D and 3D visual feedback control. This strategy allows to overcome the problem of sensor latency and to address the problem of control task failure due to visual features leaving the camera field of view. In particular, a Dual Rate Kalman Filter is used to generate inter-sample estimations of the visual features to deal with the problem of vision sensor latency, whereas a Dual Rate Extended Kalman Filter Smoother is used to generate more convenient visual features trajectories in the image plane. Both 2D and 3D visual feedback control approaches are widely analyzed throughout the paper, as well as the overall system performance using different visual feedback controllers, providing a set of results that highlight the improvements in terms of solution reachability, robustness, and time domain response. The proposed control strategy has been validated on an industrial system with hard real-time limitations, consisting of a 6 DOF industrial manipulator, a 5 MP camera, and a PLC as controller.

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LED chip accurate positioning control based on visual servo using dual rate adaptive fading Kalman filter

Cited by 14 publications

References 25 publications

Unbalance identification for a practical turbofan engine using augmented Kalman filter improved with the convergence criterion

Unbalance identification for a practical turbofan engine using augmented Kalman filter improved with the convergence criterion

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Generalization of reference filtering control strategy for 2D/3D visual feedback control of industrial robot manipulators

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