Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 9, Pages 9597-9608Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3058997
Keywords
Fading channels; Stochastic processes; Nonlinear systems; Adaptation models; Mathematical model; Convergence; Time-domain analysis; Event-triggered mechanism; fading channels; iterative learning control (ILC); model free adaptive control (MFAC)
Categories
Funding
- National Natural Science Foundation of China [U1804147, 61573129, 61833001, 62003133]
- Innovative Scientists and Technicians Team of Henan Polytechnic University [T2019-2]
- Innovative Scientists and Technicians Team of Henan Provincial High Education [20IRTSTHN019]
- Natural Science Foundation of Henan Province of China [202300410177]
- Fundamental Research Funds for the Universities of Henan Province [NSFRF180335]
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This article explores the problem of event-triggered model-free adaptive iterative learning control for a class of nonlinear systems over fading channels. An event-triggered condition is constructed to save communication resources in iterations, and a control method based on faded outputs is proposed. The boundedness of tracking error is verified using Lyapunov function in rigorous analysis and convergence proof.
This article investigates the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels. The fading phenomenon existing in output channels is modeled as an independent Gaussian distribution with mathematical expectation and variance. An event-triggered condition along both iteration domain and time domain is constructed in order to save the communication resources in the iteration. The considered nonlinear system is converted into an equivalent linearization model and then the event-triggered MFAILC independent of the system model is constructed with the faded outputs. Rigorous analysis and convergence proof are developed to verify the ultimately boundedness of the tracking error by using the Lyapunov function. Finally, the effectiveness of the presented algorithm is demonstrated with a numerical example and a velocity tracking control example of wheeled mobile robots (WMRs).
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