Distributed Neural Fixed-Time Consensus Control of Uncertain Multiple Euler-Lagrange Systems With Event-Triggered Mechanism
Chen Wang, Haoran Zhan, Qing Guo, Tieshan Li
Abstract
Euler-Lagrange systems are often used to describe many practical plants with strong coupling nonlinearities, such as robotic manipulators, autonomous surface vehicles, and wearable exoskeletons. Since a single Euler-Lagrange system has limited capabilities, it is imperative to construct multiple Euler-Lagrange systems (MELSs) to collaborate on complex operational missions. However, most of the existing controllers for MELSs only obtain asymptotic stabilization, which cannot guarantee the system states fast stabilization. Furthermore, the sampling and transmission of data at high frequencies within the MELSs can lead to network congestion, thus affecting the stability of the system. Hence, a distributed neural fixed-time consensus controller with an event-triggered mechanism is presented to not only improve the leader-following consensus speed under a directed communication graph, but also save the communication resources of the MELSs. Ultimately, the effectiveness of the proposed controller is verified through simulations and experimental results about a multiple manipulator system.