AUTHOR=Liu Chao , Han Limin , Yan Bocheng , Niu Ben , Li Shengtao , Liu Xiaomei 

TITLE=Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

JOURNAL=Frontiers in Physics

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1212564

DOI=10.3389/fphy.2023.1212564

ISSN=2296-424X

ABSTRACT=This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems (MASs). First of all, each agent system considered in this article is the nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method can not be directly applied to design controller. However, by applying the unique structure of Gaussian function in radial basis function neural networks (RBF NNs), the challenges in controller design caused by the nonstrict-feedback system mentioned above have been overcome. Secondly, the problem about unknown nonlinearities in the system is solved by the approximation property of RBF NNs technology. In addition, the traditional backstepping approach often leads to an "explosion of complexity" resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, the new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme.