LMI based neurocontroller for guaranteed cost control of discrete-time uncertain system

This paper investigates the application of neural networks to the guaranteed cost control problem of discrete time uncertain system. Based on the linear matrix inequality (LMI) design approach, a class of a state feedback controller is established, and the sufficient conditions for the existence of guaranteed cost controller are derived by making use of the LMI. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. It is newly shown that although the neurocontroller is included in the discrete-time uncertain system, the robust stability for the closed-loop system and the reduction of the cost performance are attained.