A Parameter-Robust, Weighting-Factor-Less Model-Free Predictive Current Control of Induction-Motor Drives Using an Extended State Observer

Field-oriented control and direct torque control of induction-motor (IM) drives, and conventional finite-control-set model predictive control (FCS-MPC), all depend explicitly on machine parameters that drift with temperature and saturation, degrading current quality and, for the predictive case, even threatening closed-loop stability. This paper develops a parameter-robust, weighting-factor-less model-free predictive current control (MFPCC) for IM drives, in which the lumped disturbance of an ultra-local model is reconstructed online by a linear extended-state observer (ESO) and the inverter state is chosen by a current-only cost function. A two-step (k+2) prediction horizon is utilized to explicitly compensate for the one-sample microprocessor computation-and-actuation delay inherent to digital predictive control. The speed loop is governed by a model-free intelligent-proportional controller; thus, no proportional-integral current regulator and no machine parameter appear in the control path, with the single exception of the ultra-local model design gain α, which is fixed at the nominal input gain 1/σ Ls to establish parameter robustness. A comparative disturbance-observer study shows that the finite-difference estimator amplifies measurement noise and that an adaptive super-twisting observer cannot track the fast back electromotive force (back-EMF)-dominated lumped term of the IM at practical sampling rates, whereas the ESO tracks it faithfully (correlation 0.98). Under full inverter non-idealities (3 µs dead-time, 12-bit quantization, 50 mA offset, and 3% DC-link ripple), the proposed scheme attains 3.2% stator-current THD, matching an accurately tuned model-based FCS-MPC (3.9%) and significantly outperforming the finite-difference baseline (11.3%) while using none of the five machine parameters. Closed-loop stability and robust tracking are confirmed via a 50-run Monte-Carlo study, while a DSP timing estimate confirms real-time feasibility.