Induction Machine Transient Energy Loss Minimization using Neural Networks

Reducing losses in an induction machine during transient periods is highly desirable in applications where the torque-speed operating point is continuously changing. Optimal control theory can be used to find control trajectories that minimize the cost function defining the losses. However, solving optimal control problems is generally impractical to realize in real time, especially if the underlying system dynamics are nonlinear. In this work, a feed forward neural network is trained so as to emulate the solution of the loss minimizing optimal control problem in real time. Simulation results using a 6th order model are presented comparing the performance of the proposed neural network control with that of standard field oriented control. It is found that transient energy losses are reduced.