Nonlinear Cooperative Output Regulation with Input Delay Compensation

TL;DR

This paper develops a novel periodic event-triggered control framework for nonlinear multi-agent systems with input delays, ensuring Zeno-free behavior, reduced communication, and exponential stability through observer design, predictor feedback, and Lyapunov methods.

Contribution

It introduces a new asynchronous distributed observer and a predictor feedback control method tailored for nonlinear MASs with input delay, utilizing periodic event-triggered mechanisms.

Findings

Proposed a Zeno-free periodic event-triggered control scheme.

Achieved exponential stability of the nonlinear MASs with input delay.

Validated the effectiveness through simulations.

Abstract

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results.