Robust Adaptive Finite-time Consensus Tracking Protocols for a Group of Nonlinear Autonomous Systems

In this paper, distributed robust adaptive finite-time tracking protocols are developed by using consensus mechanism for a group of second-order leader-follower nonlinear autonomous systems with the presence of uncertainty. The consensus protocols are designed by using Lyapunov and nonlinear terminal sliding mode theory. The sliding mode surface is designed by using the states of the local and neighboring systems that are shared via local area communication networks. Robust and adaptive learning algorithms are used with the protocol to learn and compensate uncertainty associated with leader and follower systems dynamics. Adaptive learning algorithms are used to adapt with the input of the leader system. Lyapunov and matrix theory together with terminal sliding mode control strategy uses to show the convergence of the finite-time consensus property. Analysis shows that robust consensus protocol allows the systems to share their states information and reach an agreement to track the states of the leader system in finite-time. The protocols design and analysis do not require the bound of the uncertainty associated with the followers and leader systems dynamics. The protocol does not use the exact bound of the input of the leader system. Finally, evaluation results are presented to demonstrate the validity of the proposed design for real-time applications.