Event-Triggered Model-Free Adaptive Iterative Learning Control for a Class of Nonlinear Systems Over Fading Channels

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).

Automated summary

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.