Event-triggered data-driven control of discrete-time nonlinear systems with unknown disturbance.

This paper is dedicated to event-triggered data-driven control of nonlinear systems with unknown disturbance via model free iterative learning approach. An extended state observer is employed to reconstruct the disturbance in system output. An event-triggered model free iterative learning control strategy is constructed by system input, system output and the reconstructed disturbance. Sufficient conditions are proposed to make the resultant tracking error system be uniform ultimate bounded. Simulation examples are provided to validate the effectiveness of the proposed scheme. • An extended state observer is employed to estimate the unknown disturbance in system output. • An event-triggered model free iterative learning control scheme is presented by integrating tracking error, input error and the estimated disturbance. • Sufficient conditions are proposed to make the resultant event-triggered model free iterative learning tracking error system be uniform ultimate bounded. [ABSTRACT FROM AUTHOR]