H_∞ consensus of nonlinear multi-agent systems based on T-S fuzzy models

In general, due to some limitations of nonlinear control methods, it is difficult to analyze control performance for nonlinear multi-agent network. The T-S fuzzy model-based approach is often introduced to help solve the performance analysis in nonlinear systems, but the problem of nonlinear follower agents approaching a time-varying leader is difficult to be formulated by using the general T-S fuzzy modeling method. In this paper, a novel T-S fuzzy modeling method is proposed, and the error dynamics between the states of agents and the leader signal, evolving according to an isolated unforced nonlinear agent model, is described by a set of T-S fuzzy models. Based on the model, leader-following consensus algorithm is conveniently designed so that under external disturbances, all the follower agents achieve consensus with the leader guaranteeing a prescribed disturbance attenuation level in H_∞ sense. Finally, simulations with chaotic dynamic systems and sinusoidal functions are presented, and by applying the obtained results to the initial nonlinear systems, the effectiveness of the obtained results is illustrated.