Predefined Time Consensus Control of Nonlinear Multi-Agent Systems for Industry 5.0

Intelligent Internet of Things and its diverse application domains are transforming industry significantly. In an industry assembly task of a multi-robot system, multi-agent system (MAS) coordinate control used in intelligent industry 5.0 systems is a critical foundation of robot control for enhanced safety and stability, increased electricity economy. MAS consensus control encounters difficult problems such as external disturbances and unknown dynamics. To overcome these deficiencies, a predefined-time adaptive consensus control scheme for a class of nonlinear multi-agent systems with unknown dynamics and external disturbances is proposed in this paper. First, two specific time-varying functions and the corresponding transformation form are constructed. Next, a predefined-time adaptive distributed controller is designed based on the backstepping method. Furthermore, an adaptive neural network is developed to handle the external disturbances and the unknown dynamics to improve the robustness of the controller. Additionally, it is proven that the errors can converge to a small neighborhood of zero using the Lyapunov stability theorem. Finally, the simulation results show that the proposed control scheme has a shorter settling time and smaller tracking errors at predefined time.