Linear quadratic state feedback and robust neural network estimator for field-oriented-controlled induction motors

Abstract

A field-oriented control scheme for an induction motor with a linear quadratic optimal regulator and a robust neural network estimator is proposed. The state feedback is designed by using the synchronous frame motor model. The number of the states is increased in order to take into account the presence of two integrators on the flux and torque errors. The resulting model is suitably simplified and the corresponding approximations are discussed. The procedure proposed is shown to be suitable also for the design of the state feedback via pole placement technique. A comparison with standard proportional integral regulators is provided. The rotor flux is estimated by using a robust neural network observer. The network training set is suitably designed in order to preserve the drive effectiveness also in the presence of large parameter uncertainties. The robust neural observer is compared with an extended Kalman filter and a standard neural network observer. Using a 250-kW induction motor as a case study, the simulation results show the effectiveness of the proposed solution, both during transient and steady-state operating conditions.

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