Distributed formation control for multiple non-holonomic wheeled mobile robots with velocity constraint by using improved data-driven iterative learning

• Published in: Applied mathematics and computation Vol. 395; p. 125829
• Main Authors: Hou, Rui, Cui, Lizhi, Bu, Xuhui, Yang, Junqi
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.04.2021
• Subjects: Data-driven control; Formation control; Graph theory; Iterative learning control; Nonholonomic wheeled mobile robots; Velocity constraint; 99-00; 00-01; Nonholonomic wheeled mobile robots; Velocity constraint; Formation control; Graph theory; Data-driven control; Iterative learning control
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2020.125829

•The formation problem of mobile robot with unknown dynamic model is investigated.•Proportional integral type data-driven iterative learning formation control algorithm is proposed.•The effect of speed constraint on the convergence for formation and driving force updating algorithm is studied.•Dynamic control algorithm is designed merely utilizing the I/O of model. In this paper, a distributed proportional-integral data-driven iterative learning control (PI-DDILC) algorithm is developed to achieve the formation problem for non-holonomic and velocity constrained wheeled mobile robots (WMRs) under repeatable operation environment. Firstly, the formation problem is transformed into a tracking problem with a certain deviation from the reference trajectory. And a distributed kinematics control algorithm is designed by using leader-follower strategy and graph theory. Then, to solve the problem of unknown dynamic model, the relationship between WMR’s output and input is first derived by utilizing the iteration-domain dynamical linearization technique. After that, the improved data-driven iterative learning dynamics control algorithm is provided, which includes both proportional and integral terms along the iteration axis. This algorithm can ensure a group of WMRs to realize formation and only use I/O data of each WMR. Compared with DDILC, PI-DDILC can significantly enhance the response speed and transient performance of WMR system formation. The excellence of the improved algorithm is certified by simulation, and a performance index is designed to quantify the results of the two on formation performance.