Induction machine transient energy loss minimization using neural networks

Comparative Study of Two Nonlinear Control Strategies of Induction Motors considering Heating and Magnetic Saturation

“…By analyzing (21), it is easy to see that the feedback term that linearizes the speed dynamics can be defned as follows:…”

“…For example, a PID controller is used in [2,3,6] with adaptation of some parameters. Advanced control laws were also applied to IM such as fuzzy logic [12,13], backstepping technique [14][15][16][17], artifcial neural networks (ANNs) [18][19][20][21], sliding mode (SM) [22][23][24][25][26], feedback linearization technique (FLT) [27,28], and more. For sensorless control of IM without saturation phenomena, the authors in [12] suggest a model reference adaptive system (MRAS) speed estimator that uses type-1 and type-2 fuzzy logic controllers, with the type-2 fuzzy logic controller being proposed to handle higher degrees of uncertainty and improve performance under various operating conditions.…”

“…In [17], a maximum torque per ampere (MTPA) method based on the backstepping controller is presented for IM drives, taking into account the efects of both iron loss and saturation; however, the saturation of the main fux path was defned as a function of magnetizing current without physical meaning. In [18][19][20][21], the authors introduce the artifcial neural networks (ANNs) for the control of IM using FOC principles, but they require data for training and consume a lot of memory resources. Te authors in [22] present a model-based lossminimization approach, which is combined with a backstepping direct torque control of the IM and describes a sliding-mode rotor fux observer to calculate the rotor speed, rotor time constant, and rotor fux space vector simultaneously.…”