Real-time decentralized inverse optimal neural control for a Shrimp robot

This paper deals with a decentralized inverse optimal neural controller for MIMO discrete-time unknown nonlinear systems, in a presence of external disturbances and parameter uncertainties. It uses two techniques: first, an identifier based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) algorithm; second, on the basis of the real identifier a controller which uses inverse optimal control, is designed to avoid solving the Hamilton Jacobi Bellman (HJB) equation. The proposed scheme is implemented in real-time to control a Shrimp robot.