Receding horizon disturbance attenuation for Takagi-Sugeno fuzzy switched dynamic neural networks

In this paper, we propose a new receding horizon disturbance attenuator (RHDA) for Takagi-Sugeno (T-S) fuzzy switched Hopfield neural networks with external disturbance. First, a new set of linear matrix inequality (LMI) conditions is proposed for the finite terminal weighting matrix of the receding horizon cost function with a cross term. Second, under this condition, we show that the proposed RHDA attenuates the effect of external disturbance on T-S fuzzy switched Hopfield neural networks with a guaranteed infinite horizon ℋ_∞ performance. In addition, we prove that the proposed RHDA guarantees internal stability in closed-loop systems. A numerical example is presented to describe the effectiveness of the proposed RHDA scheme.