Iterative learning control for multi-agent formation

This paper employs iterative learning control scheme to generate a sequence of control signals for multi-agent formation control. It is assumed that individual agent of a group of multi-agents is governed by nonlinear dynamics, which could be known in part; in such case, we would like to find control sequences of individual agents such that they form a desired formation with respect to other agents, from initial starting points to final stop points. That is, we would like to ensure that the multi-agents form relative desired states with respect to other agents along the desired trajectory. The algorithm established in this paper can be used to find a control sequence of multi-agent systems for keeping relative formation, in off-line tuning manner. The utility of the algorithm established in this paper can be therefore used for finding optimal control strategy of nonlinear dynamic systems with partially available system information.