Distributed adaptive consensus tracking for a class of multi-agent systems via output feedback approach under switching topologies

The consensus tracking problem is discussed for a class of multi-agent systems (MASs) in non-affine pure-feedback form via output feedback approach under switching directed topologies. Observers are employed to reconstruct state information of the system, and then a consensus tracking control scheme is presented by the backstepping method combining with dynamic surface control (DSC) technique, neural networks (NNs), and graph theory. The main advantage of the proposed strategy is that it only relies on the output signals of individual agents in communication graph and it removes the requirement for exact priori knowledge about parameters of agents. Moreover, by introducing DSC technology, it avoids the well known problem of ‘explosion of complexity’ that conventional backstepping method suffers from along with the increasement of the degree of individual agents. It is proven that the designed output feedback control scheme guarantees consensus errors are cooperatively semiglobally uniformly ultimately bounded and converge to neighborhood of the origin by suitable choice of design parameters. Simulation results are presented to demonstrate the effectiveness of the proposed control approach.