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Volume 9 Issue 6
Jun.  2022

IEEE/CAA Journal of Automatica Sinica

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X. Ge, Q.-L. Han, J. Wang, and  X.-M. Zhang,  “A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 990–1004, Jun. 2022. doi: 10.1109/JAS.2021.1004263
Citation: X. Ge, Q.-L. Han, J. Wang, and  X.-M. Zhang,  “A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 990–1004, Jun. 2022. doi: 10.1109/JAS.2021.1004263

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

doi: 10.1109/JAS.2021.1004263
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  • This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

     

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    Highlights

    • A scalable and collision-free adaptive formation tracking control protocol
    • An efficient built-in collision avoidance mechanism
    • A design algorithm of the desired adaptive formation tracking control laws
    • A multi-vehicle formation tracking case study in an obstacle-laden 2D plane
    • A collision-free vehicle platooning case study in a longitudinal plane

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