Formation Control for Second-order Discrete-time Multi-agent Systems with Nonlinear Dynamics Using Neural Networks

Time-varing formation control problems for second-order discrete-time heterogeneous multi-agent systems with unknown nonlinear dynamics are studied. Distributed control protocol is presented where neural networks are utilized to approximate different unknown nonlinear dynamics of each agent in an on-line manner. The stability and formation feasibility of the multi-agent systems under the proposed protocol are studied using the common Lyapunov stability theory, graph theory, and state-space methods. Sufficient conditions for multi-agent systems with undirected interaction topologies to achieve desired time-varying formation are provided. A numerical simulation is presented to demonstrate the effectiveness of the proposed protocol.