Nonlinear filter-based fuzzy adaptive iterative learning consensus for multi-agent systems with asymmetric dead-zone output
Mengdan Liang, Junmin Li · International Journal of Systems Science · 2025
This work studies the exact consensus control problem for a class of repeated nonlinear non-strict feedback multi-agent systems with unknown control gains and asymmetric dead-zone outputs. By a new smooth approximation method for asymmetric non-smooth dead-zone nonlinearity and combining the projection operator, the nonlinear-filter-based adaptive fuzzy iterative learning compensation mechanism is proposed for reducing the influence of non-smooth dead-zone outputs and unknown control gains. Further, it also avoids the inherent complexity explosion and the boundary error caused by the introduced filter when the control gains are unknown. In conclusion, under the proposed fuzzy adaptive iterative learning consensus scheme, all the follower agents will exactly track the leader on the finite time interval. Finally, three simulation examples would demonstrate the performance of our new algorithm.