Iterative Learning Control for Network Data Dropout in Nonlinear System
- 127 Downloads
The paper presents iterative learning control for data dropout in nonlinear system. The parallel distribution compensation method is used to determine the T-S nonlinear model and the nonlinear model is converted into local linear model. Assuming the probability of data loss is known. It is assumed that the probability of data loss is known, and the loss of data is described using a sequence that satisfies the Bernoulli distribution. The design of the learning control controller for linear discrete systems with data loss is studied. The iterative learning controller for data dropout is designed with the T-S model. The iterative learning controller designed has expected convergence characteristics and quadratic performance index. The simulation results show that the design method is effective.
KeywordsData dropout Iterative learning control T-S model
The work was supported by the Hechi University Foundation (XJ2016ZD004), Hechi university Youth teacher Foundation(XJ2017QN08), the Projection of Environment Master Foundation (2017HJA001, 2017HJB001), The important project of the New Century Teaching Reform Project in Guangxi(2010JGZ033), Guangxi Youth teacher Foundation(2018KY0459).
All authors contributed equally and significantly in writing this article. All authors read and approved the final manuscript.
Compliance with Ethical Standards
Conflict of interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
- 6.JA. Meda-Campa˜na, B. Castillo-Toledo, V. Z´u˜niga V: On the nonlinear fuzzy regulation for under-actuated systems. In Proceedings IEEE International Conference on Fuzzy System, pages 2195–2202, Vancouver, BC, Canada, Jul. 16–21, 2006.Google Scholar
- 12.T. Taniguchi, K. Tanaka, K. Yamafuji: Nonlinear model following control via T-S fuzzy model. In Proceedings of the American Control Conference. San Diego, California: Institute of Electrical and Engineers Inc., pages 1837–1841, 1999.Google Scholar
- 18.J. Liu, XE. Ruan: Networked iterative learning control for linear-time-invariant systems with random packet losses. In: Proceeding of the 35th Chinese Control Conference, pages 38–43, Chendu, China, 2016.Google Scholar
- 19.W. He and S. Ge, Robust adaptive boundary control of a vibrating string under unknown timevarying disturbance, IEEE Transactions on Control System Technology, Vol. 20, No. 1, pp. 48–58, 2012.Google Scholar
- 21.SE. Tuna: LQR-based coupling gain for synchronization of linear systems. Available: http://arxiv.org/abs/0801.3390.
- 25.V. Misra, W. Gong, D. Towsley D: Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED. In Proceeding of ACM/SIGCOMM, pages 151–160, Sweden, 2000.Google Scholar