Abstract
This chapter contributes to the convergence analysis of ILC for linear systems under general data dropouts at both measurement and actuator sides. By using a simple compensation mechanism for the dropped data, the sample path behavior of the input sequence along the iteration axis is analyzed and formulated as a Markov chain first. Based on the Markov chain, the recursion of the input error is reformulated as a switching system, and then a novel convergence proof is established in the almost sure sense under mild design conditions.
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References
Son, T.D., Pipeleers, G., Swevers, J.: Robust monotonic convergent iterative learning control. IEEE Trans. Autom. Control 61(4), 1063–1068 (2016)
Pipeleers, G., Moore, K.L.: Unified analysis of iterative learning and repetitive controllers in trial domain. IEEE Trans. Autom. Control 59(4), 953–965 (2014)
Oh, S.-K., Lee, J.M.: Stochastic iterative learning control for discrete linear time-invariant system with batch-varying reference trajectories. J. Process Control 36, 64–78 (2015)
Bu, X., Yu, F., Hou, Z., Wang F.: Terative learning control for a class of nonlinear systems with random packet losses. Nonlinear Anal. Real World Appl. 14(1), 567–580 (2013)
Pan, Y.-J., Marquez, H.J., Chen, T., Sheng, L.: Effects of network communications on a class of learning controlled non-linear systems. Int. J. Syst. Sci. 40(7), 757–767 (2009)
Huang, L.-X., Fang, Y.: Convergence analysis of wireless remote iterative learning control systems with dropout compensation. Math. Prob. Eng. 2013, 609284 (2013)
Liu, J., Ruan, X.: Networked iterative learning control approach for nonlinear systems with random communication delay. Int. J. Syst. Sci. 47(16), 3960–3969 (2016)
Zhang, B., Ye, Y., Zhou, K., Wang, D.: Case studies of filtering techniques in multirate iterative learning control. Control Eng. Pract. 26, 116–124 (2014)
Shen, D., Jin, Y., Xu, Y.: Learning control for linear systems under general data dropouts at both measurement and actuator sides: a Markov chain approach. J. Franklin Inst. 354(13), 5091–5109 (2017)
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Shen, D. (2018). Two-Side Data Dropout for Linear Deterministic Systems. In: Iterative Learning Control with Passive Incomplete Information. Springer, Singapore. https://doi.org/10.1007/978-981-10-8267-2_8
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DOI: https://doi.org/10.1007/978-981-10-8267-2_8
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