Iterative learning control of distributed parameter system based on geometric analysis

Fan, Xiao-Ju; Tian, Senping; Tian, Huiping

doi:10.1109/icmlc.2009.5212730

Cited by 2 publications

(1 citation statement)

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“…Tian Senping [68][69][70][71] and others analyzed iterative learning algorithms from a geometric perspective and proposed three new nonlinear control algorithms in the form of vector triangles, which opened up a new way of thinking for the research of iterative learning control algorithms. Togai [72] applied the optimization method to the design of iterative learning control.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fast Iterative Learning Control for Linear Discrete Systems with Parametric Disturbance and Measurement Noise

Lei

Riaz

Manzoor³

2022

Mathematical Problems in Engineering

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Precise industrial control technology is constantly in need of accurate and strong control. Error convergence for a typical linear system is very minimal when using a conventional iterative learning control strategy. This study develops a quick iterative learning control law to address this issue. We have presented a new PD iterative learning control approach which is basically grounded on backward error and control parameter rectification for a class of linear discrete time-invariant (LDTI) systems. We have deliberated the repetitive system, which has constraint disturbance and measurement noise. First, we have developed a form of the faster learning law along with a full explanation of the algorithm’s control factor generation process. And then, using the vector method in conjunction with the theory of spectral radius, sufficient conditions for the algorithm’s convergence are introduced for parameter estimation with no noise, parameter uncertainty but excluding the noise, parameter uncertainty with small perturbations, and noise in four different scenarios. Eventually, results show that convergence depends on the control law’s learning factor, the correction term, the factor of association, and the learning interval. Ultimately, the simulation results indicate that suggested approach has a faster error convergence as compared with classical PD algorithm.

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Section: Introductionmentioning

confidence: 99%