Distributed Event-Triggered Control for Cooperative Output Regulation of Multiagent Systems With an Online Estimation Algorithm

Fully Event-Triggered Practical Leader-Following Consensus of Multiple Euler-Lagrange Systems Over Switching Networks

In this article, a fully event-triggered adaptive distributed control law is developed for solving the practical leader-following consensus problem (LFCP) of a group of uncertain Euler-Lagrange (EL) systems. An event-triggered output-based distributed observer (OBDO) is established first to estimate the state variable of the leader system over jointly connected switching networks (JCSNs). Then, the event-triggered adaptive control law is designed to exclude the Zeno phenomenon and to make the steady-state consensus error smaller than any specified value by adjusting some design parameters. Compared with the existing results, the proposed event-triggered control law only depends on the output of the leader system, works over JCSNs, and, moreover, is fully event-triggered so that it can be directly implemented in a digital platform. The overall design is illustrated using a group of two-link manipulators.

Event-triggered controller design for LTI systems: A distributed interval observer-based approach

This paper investigates the distributed event-triggered control design problem for networked linear time-invariant (LTI) systems with partially measurable states, in the presence of bounded external disturbances. A distributed interval observer-based event-triggered control scheme has been proposed, the main features of this scheme lie in a) designing a novel distributed interval observer to deal with the unknown states and bound disturbances problem; b) proposing a distributed feedback control method using interval estimation information, the partially measurable limitation has been solved; c) introducing a decreased time-varying function with dead-zone modification to avoid the Zeno behavior. Moreover, sufficient conditions for the stability of closed-loop systems are given in the Lyapunov sense by using matrix inequalities and transmission strategy. Finally, the uniformly ultimately bounded stability and Zeno behavior avoidance of the proposed approach have been numerically demonstrated.

Distributed Localization for Multi-Agent Systems With Random Noise Based on Iterative Learning

This article is concerned with the real-time localization problem for the dynamic multi-agent systems with measurement and communication noises under directed graphs. The barycentric coordinates are introduced to describe the relative position between agents. A novel robust distributed localization estimation algorithm based on iterative learning is proposed. The relative-distance unbiased estimator constructed from the historical iterative information is used to suppress the measurement noise. The designed stochastic approximation method with two iterative-varying gains is used to inhibit the communication noise. Under the zero-mean and independent distributed conditions on the measurement and communication noises, the asymptotic convergence of the proposed methods is derived. The numerical simulation and the QBot-2e robot experiment are conducted to test and verify the effectiveness and the practicability of the proposed methods.