Iterative Learning Containment Control under Fading Communication

This paper studies containment control problems with fading channels for linear time-variant multi-agent systems (MASs). The fading channels are modeled by multiplicative and additive random variables. An Iterative Learning Containment Control (ILCC) algorithm is proposed to solve the containment control problem. By designing a decreasing stepsize and an appropriate learning gain, the fading problem is asymptotically eliminated along the iteration axis. As a result, all followers are driven into the convex hull spanned by the leaders, and the containment errors approach zero in the mean square sense. The simulation experiment verifies the effectiveness of the control law.