Reliable robust controller design for nonlinear state-delayed systems based on neural networks

An approach is investigated for the adaptive H_infin control design for a class of nonlinear state-delayed systems. The nonlinear term is approximated by a linearly parameterized neural networks(LPNN). A linear state feedback H_infin control law is presented. An adaptive weight adjustment mechanism for the neural networks is developed to ensure H_infin regulation performance. It is shown that the control gain matrices and be transformed into a standard linear matrix inequality problem and solved via a developed recurrent neural network