Abstract
This paper investigates the leader-following formation tracking problem (FTP) for multiple nonholonomic agent systems (MNASs) in the presence of external disturbances and parametric uncertainties, where both the kinematics and dynamics of the agents are taken into consideration. A novel finite-time distributed controller-estimator algorithm (DCEA) is designed to handle such a challenging problem. Based on Lyapunov stability method, the sufficient conditions for finite-time stability of the closed-loop system are derived. Finally, the simulation results are presented to demonstrate the effectiveness and the robustness of the proposed DCEA.
Highlights
In the past decades, the cooperative control of multi-agent systems has attracted great attention due to their potential applications in [1]–[4]
Motivated by the above discussions, we propose a novel finite-time distributed controller-estimator algorithm (DCEA) to solve such a challenging problem, which is constructed by the distributed estimator layer and the local control layer
(2) A novel finite-time DCEA is proposed under the hierarchical control framework and successfully address the leader-following formation tracking problem (FTP) for the multiple nonholonomic agent systems (MNASs), where the radial basis function (RBF) neural network is introduced to handle the parametric uncertainties
Summary
The cooperative control of multi-agent systems has attracted great attention due to their potential applications in [1]–[4]. The convergence rate is important when evaluating the working performance of the designed controllers It motivates a group of researches on finite-time control for MNASs [41]–[43] for achieving faster and more accurate convergence. (2) A novel finite-time DCEA is proposed under the hierarchical control framework and successfully address the leader-following FTP for the MNAS, where the RBF neural network is introduced to handle the parametric uncertainties. AT , A−1 denotes the transposition and inverse of matrix A, respectively. λmax(M ) is the maximum eigenvalue of the matrix M
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