Convergence properties of two networked iterative learning control schemes for discrete-time systems with random packet dropout

Liu, Jian; Ruan, Xiaoe

doi:10.1080/00207721.2018.1509244

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…19 Furthermore, some authors developed ILC controllers based on the iteration domain compensation to improve the convergence of different ILC systems with data losses in both channels. 20,21 It is worth noting that in addition to research on the loss of output data, 11,[22][23][24][25] there is also research on the simultaneous loss of input and output data. 12,15,16,18,20,21,[26][27][28][29] For instance, the time domain compensation was utilized to improve the convergence of ILC systems with data losses in both channels.…”

Section: Related Workmentioning

confidence: 99%

“…20,21 It is worth noting that in addition to research on the loss of output data, 11,[22][23][24][25] there is also research on the simultaneous loss of input and output data. 12,15,16,18,20,21,[26][27][28][29] For instance, the time domain compensation was utilized to improve the convergence of ILC systems with data losses in both channels. 18,26 Meanwhile, the iteration domain compensation was adopted to ensure that tracking errors gradually converge to zero as iteration number increases.…”

Section: Related Workmentioning

confidence: 99%

“…In their study, Huang et al analyzed the value of system transfer matrices to demonstrate the convergence of discrete linear ILC systems with random input data losses 19 . Furthermore, some authors developed ILC controllers based on the iteration domain compensation to improve the convergence of different ILC systems with data losses in both channels 20,21 …”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that in addition to research on the loss of output data, 11,22–25 there is also research on the simultaneous loss of input and output data 12,15,16,18,20,21,26–29 . For instance, the time domain compensation was utilized to improve the convergence of ILC systems with data losses in both channels 18,26 .…”

Section: Introductionmentioning

confidence: 99%

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Optimal state filters for networked iterative learning control systems with data losses and noises

Guo,

Huang,

Sun

et al. 2024

Adaptive Control & Signal

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SummaryData losses and noises in both forward and feedback channels significantly impact the convergence of networked iterative learning control (ILC) systems. To address this issue, this article considers a class of linear time‐invariant objects controlled by proportional ILC controllers, an optimal state filter is then designed at the ILC controller side that aims to guarantee the convergence of the input transmitted by ILC controllers. First, two data transmission processes are introduced to account for the effects of data losses and noises. Second, a filtering model is established utilizing only the object information and the aforementioned data transmission processes. Third, the optimal state filter is designed on the basis of the orthogonal projection principle. This filtered state facilitates the acquisition of actual output errors, thus improving the convergence of the input transmitted by ILC controllers. Simulation results demonstrate the effectiveness of the proposed state filters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal state filters for networked iterative learning control systems with data losses and noises

Guo,

Huang,

Sun

et al. 2024

Adaptive Control & Signal

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Data‐based iterative learning mechanism for unknown input‐output coupling parameters/matrices

Liu

Chen

Ruan

et al. 2019

Intl J Robust & Nonlinear

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SummaryIn this paper, we explore how to get the information of input‐output coupling parameters (IOCPs) for a class of uncertain discrete‐time systems by using iterative learning technique. Firstly, by taking advantage of repetitiveness of control system and informative input and output data, we design an iterative learning scheme for unknown IOCPs. It is shown that we can get the exact values of IOCPs one by one through running the repetitive system T+1 times if the control system is with identical initial state and noise free. Secondly, we give the iterative learning scheme for unknown IOCPs in the presence of measurement noise, system noise, or initial state drift and analyze the influence factors on the performance of developed iterative learning scheme. Meanwhile, we introduce the maximum allowable control deviation into the iterative learning mechanism to minimize the negative impact of noise on the performance of learning scheme and to enhance the robust of iterative learning scheme. Thirdly, for a class of multiple‐input–multiple‐output systems, we also develop iterative learning mechanism for unknown input‐output coupling matrices. Finally, an illustrative example is given to demonstrate the effectiveness of proposed iterative learning scheme.

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Networked iterative learning control for systems with packet dropout and communication delay

2019

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Convergence properties of two networked iterative learning control schemes for discrete-time systems with random packet dropout

Cited by 13 publications

References 27 publications

Optimal state filters for networked iterative learning control systems with data losses and noises

Optimal state filters for networked iterative learning control systems with data losses and noises

Data‐based iterative learning mechanism for unknown input‐output coupling parameters/matrices

Networked iterative learning control for systems with packet dropout and communication delay

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