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

This paper presents a nonlinear output feedback controller design method that integrates the guaranteed cost control approach for a class of discrete-time system with parametric uncertainties and neural networks (NNs). Based on the Linear Matrix Inequality (LMI) design approach, a class of output feedback controller is established, and some sufficient conditions for the existence of guaranteed cost controller is derived. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. Although the neurocontroller is included in the uncertain system, the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than specified upper bound for all admissible uncertainty. A numerical example is given to illustrate the computational efficiency of the proposed method.