Nonlinear Dynamics

, Volume 86, Issue 1, pp 605–622 | Cite as

Distributed consensus-based formation control for nonholonomic wheeled mobile robots using adaptive neural network

  • Zhaoxia Peng
  • Guoguang Wen
  • Shichun Yang
  • Ahmed Rahmani
Original Paper


This paper investigates the distributed formation control problem for multiple nonholonomic wheeled mobile robots. A variable transformation is first proposed to convert the formation control problem into a state consensus problem. Then, when the dynamics of the mobile robots are considered, the distributed kinematic controllers and neural network torque controllers are derived for each robot such that a group of nonholonomic mobile robots asymptotically converge to a desired geometric pattern along the specified reference trajectory. The specified reference trajectory is assumed to be the trajectory of a virtual leader whose information is available to only a subset of the followers. Also the followers are assumed to have only local interaction. Moreover, the neural network torque controllers proposed in this work can tackle the dynamics of robots with unmodeled bounded disturbances and unstructured unmodeled dynamics. Some sufficient conditions are derived for accomplish the asymptotically stability of the systems based on algebraic graph theory, matrix theory, and Lyapunov control approach. Finally, simulation examples illustrate the effectiveness of the proposed controllers.


Formation control Nonholonomic wheeled robots Neural network Graph theory Filippov solution 



This work is supported by the Fundamental Research Funds for the Central Universities (Nos. YWF-14-RSC-032, YWF-15-SYS-JTXY-007, YWF-16-BJ-Y-21), by the Laboratoire international associé, and by the National Natural Science Foundation of China under Grants 61403019, 61503016.


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.School of Transportation Science and EngineeringBeihang UniversityBeijingPeople’s Republic of China
  2. 2.Beijing Engineering Center for Clean Energy and High Efficient PowerBeihang UniversityBeijingPeople’s Republic of China
  3. 3.Department of MathematicsBeijing Jiaotong UniversityBeijingPeople’s Republic of China
  4. 4.CRIStAL, UMR CNRS 9189Ecole Centrale de LilleVilleneuve d’AscqFrance

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