Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

Jin, Yanqiong; Shen, Dong

doi:10.1002/asjc.1656

Cited by 17 publications

(13 citation statements)

References 32 publications

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“…Although there are arguments about the conservativeness of such technique (Ruan et al, 2012), it is widely employed. As has been stressed by Jin and Shen (2018) that such technique merely paves a way for theoretical analysis without determining the intrinsic convergence property of the addressed algorithm. Actually, as can be seen from the proof process that introducing of a − λ t only changes the convergence speed of the ILC algorithm.…”

Section: Resultsmentioning

confidence: 99%

Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults

Yang

Ruan

Geng

2022

Transactions of the Institute of Measurement and Control

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This paper is concerned with an iterative learning fault-tolerant control strategy for discrete-time nonlinear systems where actuator faults arbitrarily occur. First, the stochastic faults occurring in multiplicative and additive manner are considered. Then, statistical behaviors of both faults-corrupted control signals from the actuator to the plant and faults-free ones from the iterative learning controller to the actuator are analyzed. Meanwhile, sufficient conditions of convergence for the proposed strategy are established by resorting to the time-weighted norm technique. Finally, two numerical examples are provided to illustrate the effectiveness and reliability of the proposed results. Both theoretical analysis and simulations indicate that the developed strategy is satisfactory in preserving decent tracking accuracy of the addressed systems subject to actuator faults.

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Section: Resultsmentioning

confidence: 99%

Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults

Yang

Ruan

Geng

2022

Transactions of the Institute of Measurement and Control

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“…For the dropped system output data, Liu and Ruan used the synchronous system output data used by the ILCr at the previous iteration to replace it, and the authors used the synchronous desired signal to replace it. It is obvious that those developed in the aforementioned works consume less network resources than the one proposed in the works of Jin and Shen and Shen and Xu does. Meanwhile, numerical experiments show that the NILC scheme developed in the work of Liu and Ruan has fast tracking speed but may have poor transient tracking performance.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, other authors proposed several NILC strategies for packet dropout of system output and input. In detail, the settlement mechanism developed in the work of Shen is equivalent to replacing the dropped output signal with the synchronous data of desired trajectory and replacing the dropped input data with zero input.…”

Section: Introductionmentioning

confidence: 99%

“…In general, this kind replacement strategy for dropped system input cannot guarantee zero‐error convergence. To overcome the aforementioned drawback, Jin and Shen and Shen and Xu addressed a settlement mechanism that can guarantee zero‐error convergence. Specifically, at the actuator side, if the input signal is lost, the actuator will use the synchronous actual input signal at the previous iteration to drive the system, otherwise the actuator will use the input signal to drive the system directly.…”

Section: Introductionmentioning

confidence: 99%

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Iterative learning control for uncertain nonlinear networked control systems with random packet dropout

Zhang

Liu

Ruan

2019

Intl J Robust & Nonlinear

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This paper proposes a novel networked iterative learning control (NILC) scheme with adjustment factor for a class of discrete-time uncertain nonlinear systems with stochastic input and output packet dropout modeled as 0-1 Bernoulli-type random variable. Firstly, the equivalence relation between the realizability of controlled system and the input-output coupling parameter (IOCP) is established. Secondly, in order to overcome the main obstacle arising from the unknown IOCP, an identification technique is developed for it. Thirdly, it is strictly proved that, under certain conditions, the tracking errors driven by the developed NILC scheme are convergent to zero along iteration direction in the sense of expectation. Finally, an example is given to demonstrate the effectiveness of the proposed NILC scheme and the merits of adjustment factor. KEYWORDS iterative learning control, networked control systems, stochastic packet dropouts, uncertain nonlinear systems, unknown input-output coupling parameter Int J Robust Nonlinear Control. 2019;29:3529-3546.wileyonlinelibrary.com/journal/rnc

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“…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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Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

Cited by 17 publications

References 32 publications

Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults

Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults

Iterative learning control for uncertain nonlinear networked control systems with random packet dropout

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

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