Iterative Learning Consensus for Nonstrict Feedback Multiagent Systems With Unknown Control Direction and Saturation Input

This article addresses the adaptive iterative learning control consensus problem for a class of unknown nonlinear high-order nonstrict feedback multiagent systems with partially unknown virtual and actual control directions and saturation inputs. Due to the unknown nonlinear dynamics of all follower agents, fuzzy logic systems combined with adaptive way are employed to design control protocol. And the Nussbaum-gain method is utilized to deal with partially unknown virtual and actual control directions in each step of the backstepping design procedure. With backstepping design process constructing adaptive fuzzy iterative learning control scheme for each agent, our proposed new control algorithm ensures that the outputs of all follower agents can accurately track the leader on finite time $[ {0,T} ]$. Finally, the performance of our new algorithm is demonstrated by two simulation examples.

Automated summary

This article proposes a solution to the adaptive iterative learning control consensus problem for a class of unknown nonlinear high-order nonstrict feedback multiagent systems with partially unknown virtual and actual control directions and saturation inputs. Fuzzy logic systems combined with adaptive way and the Nussbaum-gain method are employed to design control protocol and handle the unknown dynamics and control directions. The proposed control algorithm ensures accurate tracking of the leader by the outputs of all follower agents in finite time.