Iterative trajectory learning for highly accurate optical satellite tracking systems

Riel, Thomas; Sinn, Andreas; Schwaer, Christian; Ploner, Martin; Schitter, Georg

doi:10.1016/j.actaastro.2019.07.012

Cited by 13 publications

(3 citation statements)

References 12 publications

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“…The quaternionbased pointing modeling method is an effective way to improve the pointing accuracy of telescope control systems (Riesing et al 2018). In addition to model correction methods to improve pointing accuracy, iterative learning algorithms can also be used to address local pointing model inaccuracies and dynamic effects during tracking (Riel et al 2019). Improving the accuracy of the telescope system with well-performing control models is also one of the commonly used tools, such as acceleration feedback control, which is effective in dealing with disturbances such as wind disturbances, nonlinear disturbances, and some other unknown disturbances (Wang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Large-aperture Telescope Tracking Control Based on Time-synchronization Strategy

Li,

Ye,

Yang

et al. 2024

Res. Astron. Astrophys.

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A time-synchronization strategy for packetized transmission of target position about large-aperture telescope observation control system has been proposed in this study. Compared with the existing telescope tracking strategy, the target position packing and sending strategy based on the time synchronization method proposed in this paper has the advantages of high stability and reliability. Firstly, the telescope tracking observation control method was elaborated in this paper, including the motion pattern during telescope tracking. Then, the strategy for packetizes transmission of target positions based on time-synchronization is established and lists the detailed steps. Finally, the performance of the tracking strategy is verified using the 2.5m telescope for the simulated uniform speed star and the blind-tracking fixed star HIP31216, respectively. The test results show that the accuracy RMS of the tracking strategy proposed in this paper is less than 0.02" at 30 minutes, and the performance is much better than the design requirement of 0.3". The most important advantage of this tracking strategy is that the telescope can guarantee normal tracking for a certain period of time even if the hardware or software of the host computer is abnormal.

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Section: Introductionmentioning

confidence: 99%

Large-aperture Telescope Tracking Control Based on Time-synchronization Strategy

Li,

Ye,

Yang

et al. 2024

Res. Astron. Astrophys.

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“…The iterative learning control was first proposed by Uchiyama in 1978 and introduced by Arimoto et al 18 in 1984. This control scheme has a great practical significance in dealing with systems having nonlinear coupling, high complexity, difficulty in modeling, and high‐precision tracking 19–24 . Based on the iterative learning control scheme, Riel et al 20 investigated the tracking accuracy of an optical telescope system used for satellite tracking and laser ranging applications.…”

Section: Introductionmentioning

confidence: 99%

“…This control scheme has a great practical significance in dealing with systems having nonlinear coupling, high complexity, difficulty in modeling, and highprecision tracking. [19][20][21][22][23][24] Based on the iterative learning control scheme, Riel et al 20 investigated the tracking accuracy of an optical telescope system used for satellite tracking and laser ranging applications. Bu et al 22 proposed a distributed model-free adaptive iterative learning control method for the multiagent systems with fixed or iteration-varying topologies to perform consensus tracking.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning heterogeneous trajectory tracking for partially interdependent networks

Guo

Chai

et al. 2021

Asian Journal of Control

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Trajectory tracking is a hot topic in the field of complex systems. In this paper, based on the Lyapunov stability theory, combined with the adaptive iterative learning control method, the heterogeneous trajectory tracking of partially interdependent networks is investigated in a given finite time interval. The considered network consists of two subnets, where only the first m nodes are intercoupled. Firstly, some suitable iterative learning controllers, as well as the adaptive laws and the coupling strength learning laws, are designed for each node. Secondly, the boundedness and the convergence of the signals are proved using the proposed control strategy. Finally, some simulations are presented to illustrate the effectiveness and feasibility of the theoretical results presented in this paper. Generally, the accuracy trajectory tracking is found almost proportional to the number of iterations. When the number of iterations is fixed, with the increasing number of nodes having interdependence, the time required to track target accurately is reduced.

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Iterative Learning Consensus Tracking Control for Multi-agent Systems with Neighboring Agents’ Average Control Inputs

Wang

Liu

Shan

2022

Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control

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Iterative trajectory learning for highly accurate optical satellite tracking systems

Cited by 13 publications

References 12 publications

Large-aperture Telescope Tracking Control Based on Time-synchronization Strategy

Large-aperture Telescope Tracking Control Based on Time-synchronization Strategy

Iterative learning heterogeneous trajectory tracking for partially interdependent networks

Iterative Learning Consensus Tracking Control for Multi-agent Systems with Neighboring Agents’ Average Control Inputs

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