Adaptive consensus for heterogeneous unknown nonlinear multi-agent systems with asymmetric input dead-zone: A finite-time approach

This study deals with the adaptive finite-time consensus problem of heterogeneous multi-agent systems composed of first-order and second-order agents with unknown nonlinear dynamics and asymmetric input dead-zone under connected undirected topology. Under the proposed protocol and adaptive laws, a sliding mode variable for every agent converges to a compact set in finite time, and also the position errors and the velocity errors (for second-order agents) between any two agents converge to a small desired neighborhood of the origin in finite time. Each agent requires its states and the relative positions of its neighbors. By applying sliding mode control, the external disturbances, and the imperfect approximation of neural networks are rejected. The unknown terms of the agents’ dynamics are approximated using radial basis function neural networks. The adaptive compensator plus dead-zone is applied to overcome the asymmetric input dead-zone. Based on Lyapunov stability theory, analysis is led on stability. Different from the previous works, the global information graph is not used in the proposed protocol. Finally, our approach is examined for two examples to evaluate its performance.