Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis

An indirect adaptive consensus control method is presented for multi-agent systems (MASs) with unknown hysteresis states and input. All system that can be utilized to design the controller are measured by sensors subjected hysteresis, thus, state values inaccurate. Meanwhile, it difficult compensate input coupled hysteresis. The function from agent’s neighbors also increases difficulty of design. To eliminate influence an inverse compensated presented. problem addressed designing two laws approximate upper lower bounds coefficient. Neural networks introduced handle dynamics agent its neighbors. proposed scheme guarantee errors followers converge a predefined interval zero asymptotically. In addition, transient performance MASs further ensured. simulation examples included verify effectiveness approach.