Learning control for linear systems under general data dropouts at both measurement and actuator sides: A Markov chain approach

This article is concerned with the convergence performance of wireless networked iterative learning control (ILC) systems with data dropouts and channel noises in both sensor-to-controller and controller-to-actuator channels. In order to improve the convergence performance of such ILC systems, an optimal input filter is developed at the actuator side to estimate controller updated input with the effect of these network uncertainties. Specifically, a filtering model is constructed only by using the learning process of P-type controllers and the transmission process of both measured output data and updated input data. On the basis of this model and the orthogonality principle, the filter in the sense of linear minimum variance is designed at the actuator side so that the controller updated input could be estimated in iteration domain. The convergence performance of filtering error covariance matrix is analyzed theoretically. Moreover, because the filter design does not employ plant information, the tracking performance of any plant with P-type learning controllers can be improved by driving with the filtered input. Simulation results show that the proposed filtering method is effective.

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“…where two facts that 𝜀 k (t) is independent with 𝜂 k (t) and xk|k−1 ⊥x k|k−1 are used respectively. Substituting (24) into…”

Section: Input Filtering At the Actuator Sidementioning

confidence: 99%

“…In References 22‐24, authors researched the convergence performance of networked ILC systems with data dropouts in both sides. On the basis of Markov chain, some compensation methods were proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimal input filtering for networked iterative learning control systems with packet dropouts and channel noises in both sides

Huang

Sun

Wang

et al. 2022

Intl J Robust & Nonlinear

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“…20,21 As to the latter, inspired by the work reported in Emelianova et al, 22 the input updating process with random dropouts was modeled as a Markov chain. [23][24][25] In addition, a variety of approaches were proposed for guaranteeing the convergence performance of networked ILC systems with random data dropouts, and can be split into two categories: Kalman-type filtering methods and compensation methods. In their studies, [26][27][28] Ahn et al filtered the effect of output data dropped dependently or not through selecting the learning gain adaptively.…”

Section: Related Workmentioning

confidence: 99%

“…As to the former, the value of variables was used to describe that corresponding data was dropped or not 20,21 . As to the latter, inspired by the work reported in Emelianova et al, 22 the input updating process with random dropouts was modeled as a Markov chain 23–25 . In addition, a variety of approaches were proposed for guaranteeing the convergence performance of networked ILC systems with random data dropouts, and can be split into two categories: Kalman‐type filtering methods and compensation methods.…”

Section: Introductionmentioning

confidence: 99%

Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels

Huang

Sun

Wang

et al. 2021

Math Methods in App Sciences

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The convergence performance of wireless networked iterative learning control systems is affected by additive noises, random delays, and data dropouts in both sensor-to-controller and controller-to-actuator channels. In order to guarantee the convergence performance of such systems, an input filter is designed in front of actuators to estimate the controller updated input under the effect of those uncertainties. Specifically, a P-type learning controller is considered firstly, and then a model is developed to describe the transmission of sensor measured output data and controller updated input data under the mixed effect of those uncertainties. On the basis of state augmentation, the two developed data transmission processes are further combined with the controller learning process to build a unified filtering model. According to this filtering model and the projection theory, an optimal filter in linear minimum variance sense is designed at the actuator side to estimate the controller updated input in iteration domain.The convergence performance of the norm of filtering error covariance matrix is proved theoretically, which means driven by this accurately estimated input, the convergence performance of networked systems adopting the P-type learning controller is guaranteed. Finally, some numerical results are given to illustrate the effectiveness of the proposed method.

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“…Numerous studies have been conducted on the resilience of ILC in the presence of data dropout and robustness issues. Upon review, it becomes apparent that most ILC techniques designed to address data dropout are exclusively applicable to linear systems [13][14][15][16][17] or affine nonlinear systems. [18][19][20][21][22][23] According to Bu et al 13,14 and Shen et al [15][16][17] separate ILC models are developed for linear systems.…”

Section: Introductionmentioning

confidence: 99%

Feedforward–feedback-enhanced model-free adaptive iterative learning control with measurement disturbance and data dropout for an autonomous bus trajectory tracking system

Liu,

Huang,

Ren

et al. 2024

Science Progress

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This article presents an innovative enhanced model-free adaptive iterative learning control approach suited for autonomous bus trajectory tracking systems that may experience measurement disruptions and random data dropouts. Data loss can occur independently and randomly at different times and in different iterations with varying probabilities, leading to successive data dropouts on both the time and iteration axes. The proposed enhanced model-free adaptive iterative learning control controller incorporates a data compensation mechanism to compensate for missing data, ensuring excellent control performance. This data-driven control strategy requires only input/output data for controller design. The convergence and effectiveness of the proposed approach are verified through rigorous mathematical analysis and simulation outcomes.

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Learning control for linear systems under general data dropouts at both measurement and actuator sides: A Markov chain approach

Cited by 23 publications

References 29 publications

Optimal input filtering for networked iterative learning control systems with packet dropouts and channel noises in both sides

Optimal input filtering for networked iterative learning control systems with packet dropouts and channel noises in both sides

Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels

Feedforward–feedback-enhanced model-free adaptive iterative learning control with measurement disturbance and data dropout for an autonomous bus trajectory tracking system

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