The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. 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 are attained.

In this paper, we propose a delayed feedback guaranteed cost controller design method for uncertain linear systems with delays in states. Based on the Lyapunov method, an LMI optimization problem is formulated to design a delayed feedback controller which minimizes the upper bound of a given quadratic cost function. Numerical examples show the effectiveness of the proposed method.

This paper provides a new robust guaranteed cost controller design method for discrete parameter uncertain time delay systems. The result shows much tighter bound of guaranteed cost than that of existing paper. In order to get the optimal (minimum) value of guaranteed cost, an optimization problem is given by linear matrix inequality (LMI) technique. Also, the parameter uncertain systems with time delays in both state and control input are considered.