Neural-network-based consensus of multiple Euler-Lagrange systems with an event-triggered mechanism

TLDR: This paper addresses the consensus problem for a class of multiple Euler-Lagrange systems, where agents communicate with neighbors under an event-triggered mechanism by integrating the Lyapunov functional method, neural network approximation and adaptive control techniques.

Abstract: This paper addresses the consensus problem for a class of multiple Euler-Lagrange systems, where agents communicate with neighbors under an event-triggered mechanism. Due to the more complex dynamical characteristics, the consensus problem of multiple Euler-Lagrange systems is more challenging than that of ordinary second-order multi-agent systems. In this study, we assume that the inertia matrix, the Coriolis and centrifugal term, and the gravitational torque are totally unknown, then a protocol is derived by integrating the Lyapunov functional method, neural network approximation and adaptive control techniques. In addition, the event-triggered mechanism effectively reduces the communication traffic, and the Zeno behavior is well excluded. By a demonstrative example, the effectiveness of the protocol is illustrated.