Discrete-time decentralized inverse optimal neural control for a shrimp robot

This paper deals with an decentralized inverse optimal neural controller for discrete-time unknown nonlinear systems, in presence of external disturbances and parameter uncertainties. It is based on two techniques: first, an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm; second, a controller which on the basis of inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation. Computer simulations are presented which illustrate the effectiveness of the proposed tracking control law.