Neural Inverse Optimal Control Implementation for Induction Motors via Rapid Control Prototyping

This paper presents a discrete-time neural inverse optimal control for induction motors, which is implemented on a rapid control prototyping (RCP) system using a C2000 Microcontroller-Simulink platform. Such controller addresses the solution of three issues: system identification, trajectory tracking, and state estimation, which are solved independently. The neural controller is based on a recurrent high order neural network (RHONN), which is trained with an extended Kalman filter. The RHONN is an identifier to obtain an accurate motor model, which is robust to external disturbances and parameter variations. The inverse optimal controller is used to force the system to track a desired trajectory and to reject undesired disturbances. Moreover, the controller is based on a neural model and does not need the a-priori knowledge of motor parameters. A supertwisting observer is implemented to estimate the rotor magnetic fluxes. The hub of the RCP system is a TMS320f28069M MCU, which is an embedded combination of a 32-bit C28x DSP core and a real-time control accelerator. This Microcontroller is fully programmable from the Simulink environment. Simulation and experimental results illustrate the performance of the proposed controller and the RCP system, and a comparison with a control algorithm without the neural identifier is also included.