A framework of iterative learning control under random data dropouts: Mean square and almost sure convergence

Shen, Dong; Xu, Jian‐Xin

doi:10.1002/acs.2802

Cited by 26 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [28], authors indicated there are two kinds of methods to guarantee the convergence of ILC systems with data dropouts: Kalman filtering-based method and data compensation method. As to data compensation, this approach can be further divided into time domain compensation and iteration domain compensation.…”

Section: Convergence Speed Analysis Of the System With Successivmentioning

confidence: 99%

“…Specifically, a P-type control update algorithm was proposed in [26] for the SISO affine nonlinear system with random output data losses and unknown control direction, and a simple P-type update law was used in [27] for both linear and nonlinear cases based on stochastic approximation. In [28], authors first reviewed the recent progress on the networked ILC systems with data dropouts from the perspective of data dropout model, data dropout position and convergence meaning, respectively. After that, a general framework was proposed for the convergence analysis of three different data dropout models, namely, the stochastic sequence model, the Bernoulli variable model and the Markov chain model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convergence Analysis of Iterative Learning Control Systems Over Networks With Successive Input Data Compensation in Iteration Domain

et al. 2019

View full text Add to dashboard Cite

This paper analyzes the convergence of iterative learning control for a class of discrete-time systems over networks with successive input data compensation. Specifically, the successively dropped input data in current iterations are compensated by the one actuator received correctly with the same time instant label in the latest iteration. Through analyzing the variation of elements in the transition matrices of the input errors at the controller side, the convergence of output errors is addressed. The analysis shows the selection range of learning gain is determined by the maximum number of input data dropped successively. Moreover, the convergence of system with successive input data compensation is guaranteed by trading the convergence speed, and the more input data are successively compensated in iteration domain, the more convergence speed of the system is reduced. Finally, numerical experiments are given to corroborate the theoretical analysis.

show abstract

Section: Convergence Speed Analysis Of the System With Successivmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convergence Analysis of Iterative Learning Control Systems Over Networks With Successive Input Data Compensation in Iteration Domain

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Clearly, the random variable k ðtÞ is multiplicative to the original signals. The ILC for systems with random data dropouts has been a hot topic in the past few years [60][61][62][63][64][65][66].…”

Section: Examples Of Multiplicative Randomnessmentioning

confidence: 99%

“…Various techniques have been provided from different perspectives. A systematic design and analysis framework for three data dropout models was reported in [65] based on the stochastic approximation techniques. For the other examples, systematic frameworks are still open.…”

Section: Possible Future Directionsmentioning

confidence: 99%

A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control

Shen

2018

Un. Sys.

Self Cite

View full text Add to dashboard Cite

This paper contributes to a technical overview of recent progresses on stochastic iterative learning control (ILC), where stochastic ILC implies the learning control for systems with various random signals and factors such as stochastic noises, random data dropouts and inherent random asynchronism. The fundamental principles of ILC are first briefed with emphasis on the system formulations and typical analysis methods. Then the recent progresses on stochastic ILC are reviewed in three parts: additive randomness case, multiplicative randomness case, and coupled randomness case, respectively. Three major approaches, i.e., expectation-based method, Kalman filtering-based method, and stochastic approximation-based method, are clarified. Promising research directions are also presented for further investigation.

show abstract

“…Shen and Wang proposed a P-type control update algorithm in [20] for a SISO affine nonlinear system with random measurement data losses and unknown control direction, and proposed a simple P-type update law for both linear and nonlinear cases based on stochastic approximation [21]. Shen and Xu [22] first reviewed the recent progress on networked ILC systems in the presence of data dropouts. After that, a general framework was proposed for the convergence analysis of three different data dropout models.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Iterative Learning Control for a Class of Mobile Robot Systems With Channel Noise

et al. 2019

View full text Add to dashboard Cite

This paper considers the robust tracking problem of a unicycle type mobile robot, which is controlled remotely by an iterative learning controller over the wireless network. First, taking the effect of the channel noise into account, the nonlinear system model is formulated to describe the mobile robot controlled by a differential-type iterative learning controller. After that, the relation between the input error at the controller side and the channel noise is derived. Based on this relation, the norm of output error is derived and analyzed theoretically. The analysis reveals that the effect of channel noise is accumulated in both the iteration and time domains, and is ruled simultaneously by the sampling time in the nonlinear function of the system. Due to the sampling time that is far less than one, the accumulated effect of channel noise on the robustness of the system is suppressed significantly by the sampling time, which means that the mobile robot can track the desired trajectory without any processing. Finally, the simulation results are given to corroborate the theoretical analysis. INDEX TERMS Iterative learning control, wireless network, channel noise, mobile robot, robustness.

show abstract

A framework of iterative learning control under random data dropouts: Mean square and almost sure convergence

Cited by 26 publications

References 48 publications

Convergence Analysis of Iterative Learning Control Systems Over Networks With Successive Input Data Compensation in Iteration Domain

Convergence Analysis of Iterative Learning Control Systems Over Networks With Successive Input Data Compensation in Iteration Domain

A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control

Robustness Analysis of Iterative Learning Control for a Class of Mobile Robot Systems With Channel Noise

Contact Info

Product

Resources

About