Formation control of wheeled mobile robots with unknown information via deterministic learning
PENG Tao
LIU Cheng-jun
Abstract：This paper investigates the formation control of wheeled mobile robots(WMR)with unknown information under nonholonomic constraints.Firstly,based on the leader-follower method and the virtual structure method,the forma-tion control is transformed into the problem that the followers track their virtual leader. Secondly,a radial basis function neural network(RBF NN)is used to learning the unknown information(closed-loop system dynamics)of WMR,and a stable adaptive RBF NN controller and the stable adaptive tuning law of RBF NN parameters are derived in the sense of the Lyapunov stability theory.According to deterministic learning,a partial persistent excitation(PE)condition of some inter-nal signals in the closed-loop system is satisfied in the control process of tracking a recurrent reference trajectory,and an accurate approximation of the unknown closed-loop system dynamics is achieved by the RBF NN parameters convergence to their optimal weights. Finally,a RBF NN learning controller which effectively utilizes the learned knowledge without re-adapting the RBF NN parameters is proposed to achieve the closed-loop stability and improve the control performance, and simulation studies are included to demonstrate the correctness and effectiveness of the proposed approach.
Keywords：unknown informationmobile robot formationnonholonomic constraintsystem dynamicslearning con-trol
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 239-247 )