Command-Filter-Based Adaptive Containment Control for Stochastic Nonlinear Multi-Agent Systems
Wenbin Xiao, Hongyi Li, Renquan Lu, Wei Can Meng, Yong Kang Xu · 2020
This paper studies the finite-time containment control problem for stochastic nonlinear multi-agent systems with unmeasurable states. Under the assumption that the communication topology is fixed and directed, a distributed neuro-adaptive containment scheme is proposed to drive all followers to converge into the convex hull spanned by the dynamic leaders. In the control design, a second-order command filtered backstepping technique is adopted to deal with the explosion of complexity problem and the filtering error is compensated by an error compensation mechanism. Based on stochastic stability theory, the stability of the nonlinear multi-agent system is verified via applying the proposed scheme. Finally, the control property of the proposed strategy is validated by a numerical example.