Posture-based Leader-follower Formation for Multiple Mobile Robot Systems Using Data-based Adaptive Iterative Learning Control

Multiple mobile robot systems (MMRSs) have great potential in various fields and their reliable application depends on accurate formation among robots. Focusing on the formation problem of MMRSs, a posture-based adaptive iterative learning control scheme (PB-AILC) is proposed in this paper. Firstly, the leader-follower formation model is built. Then PB-AILC scheme is introduced to optimize the control linear and angular velocity of follower robots at each iteration based on the robots’ global posture only, guaranteeing that followers can steadily track their leader with prescribed relative distance and orientation. Meanwhile, we analyze its feasibility mathematically. Finally, numerical simulations illustrate the validity of the PB-AILC scheme and we compare PB-AILC with an iterative learning control (ILC) method to demonstrate its superiority.