This paper investigates the model predictive stabilization problem for Takagi-Sugeno (T-S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T-S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme.
- Cost monotonicity
- Linear matrix inequality (LMI)
- Model predictive stabilization
- Takagi-Sugeno (T-S) fuzzy neural networks
ASJC Scopus subject areas
- Artificial Intelligence