A comparison of compensation methods for random input data dropouts in networked iterative learning control system

Lixun Huang,Hanqing Ding,Zhe Zhang,Lijun Sun,Qiuwen Zhang

doi:10.1186/s13662-018-1935-x

Lixun Huang, Hanqing Ding + Show 3 more

Open Access

https://doi.org/10.1186/s13662-018-1935-x

Copy DOI

Abstract

In this paper, for a class of linear networked iterative learning control (ILC) systems, methods to compensate dropped input data in time or iteration domain are compared. Specifically, the transition matrices of input error at the controller side with the two methods are derived first, respectively. After that, the varieties of eigenvalues and elements in the lower triangular of the transition matrices are analyzed. Through analyzing the varieties, it can be easily found that the two methods guarantee the convergence of input error at the controller side, while only the compensation in iteration domain guarantees the convergence of input error at the actuator side. Due to the introduction of networks, the convergence of output error is determined by the input error at the actuator side. Hence, a conclusion could be made naturally that the output error converges to zero with compensation in iteration domain, while compensation in time domain cannot guarantee that. Finally, numerical experiments are given to corroborate the theoretical analysis.

Highlights

Iterative learning control (ILC) is an effective method when it is used to deal with the system that operates repetitively in a finite time interval [1,2,3,4]
Convergence has been studied in ILC from a number of different perspectives such as stochastic noise [5,6,7], initial input error [8, 9], model uncertainty [10, 11], disturbance rejection [12], and parameter optimization [13]
This paper proved that under some given conditions, the ILC can guarantee the convergence of the tracking error some packets are missing

Summary

Introduction

Iterative learning control (ILC) is an effective method when it is used to deal with the system that operates repetitively in a finite time interval [1,2,3,4]. Through the analysis of a variety of element values in the transition matrix of input vector, convergence of the networked ILC systems with data dropout compensation in time domain was improved. This method was adopted by [22] to consider the problem of ILC over networks for a class of nonlinear systems with random packet dropouts in inputs and outputs simultaneously.

Results

Conclusion