Real-time neural inverse optimal control for position trajectory tracking of an induction motor

This paper describes a neural inverse optimal control approach for a three-phase induction motor position trajectory and flux magnitude tracking. A recurrent high order neural network (RHONN) is used to identify the plant model, trained with an Extended Kalman Filter (EKF) algorithm; the control law minimize a cost functional avoiding to solve the Hamilton Jacobi Bellman (HBJ) equation. The applicability of the approach is illustrated via experimental results. The proposed scheme allows the easy integration of this kind of motors into a system of systems configuration.