Exact Consensus Control of Nonlinearly Parameterized Multi-Agent Systems With Unknown Control Directions

This paper tackles the exact consensus issue in nonlinearly parameterized multi-agent systems with unknown control directions, employing an adaptive learning control approach. The nonlinearly parameterized function is effectively compensated using neural networks and Fourier cardinal expansions. A cooperative consensus protocol is meticulously designed to achieve exact consensus through the application of iterative learning laws. The convergence of the consensus algorithm is rigorously analyzed using a composite energy function. Additionally, the robustness of the consensus algorithm is examined by introducing input disturbances to the controlled subsystem and bounded inputs to the leader. The scalability of the consensus protocol is explored through the context of formation control problems. Finally, the effectiveness of the constructed learning protocol is illustrated by simulations.