Low‐Complexity Model‐Free Predictive Torque Control for Induction Motor Drives Using an Ultralocal Model

A conventional model‐free predictive torque control (MF‐PTC) strategy suffers from computational and process complexity. This complexity arises due to the complex torque calculation, the tuning task of the nontrivial weighting factor in the cost function, and the tuning task of the non‐physical scaling factor in the iterative current prediction loop. To address these issues and enhance controller performance, a low‐complexity MF‐PTC strategy has been proposed utilizing an equivalent reference stator flux vector calculation technique based on the stator and rotor flux. An ultralocal model instead of the mathematical model of the induction motor is used to construct the model‐free predictive controller. The unknown disturbance of the ultralocal model is estimated using a linear extended state observer (ESO). Tuning the local parameters of the ESO is simplified, as it is based on a simple algebraic function rather than a complex Lyapunov function. However, the estimation of the stator flux can be influenced by variations in the stator resistance, leading to degraded performance of the proposed controller. To mitigate this issue, a resistance estimator has been designed based on a model reference adaptive system observer. Experimental results demonstrate that the performance of the proposed controller is better than that of the conventional MF‐PTC.