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

Rui Hou, Lizhi Cui, Xuhui Bu and Junqi Yang

Applied Mathematics and Computation, 2021, vol. 395, issue C

Abstract: 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.

Keywords: Formation control; Nonholonomic wheeled mobile robots; Data-driven control; Iterative learning control; Velocity constraint; Graph theory (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:395:y:2021:i:c:s0096300320307827

DOI: 10.1016/j.amc.2020.125829

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