Reformulated Predictive Torque and Flux Control with a Full-Order Adaptive Observer and Accurate Discrete-Time Models for Sensorless Induction Machine Drives

Abstract In this paper, we present a reformulation of both the predictive torque and flux control (PTC) scheme and the full-order adaptive observer (FAO) for induction machine drives. The proposed approach is based on a state-space representation expressed exclusively in terms of stator current and stator flux linkage, which simplifies the observer structure and removes the explicit dependence on rotor flux variables found in conventional sensorless formulations. This reformulated representation is applied consistently within both the FAO and PTC frameworks, and second- and higher-order discrete-time models are derived using Taylor- and Runge–Kutta–based methods to enhance numerical accuracy and dynamic performance. The resulting FAO–PTC scheme is validated through Hardware-in-the-Loop simulations, demonstrating steady-state performance comparable to conventional designs while achieving faster transient response, improved dynamic behaviour, and a reduced state-space order. Among the evaluated discretization strategies, the Taylor-based model offers the highest steady-state accuracy and the fastest convergence, albeit with slightly increased torque ripple. Overall, the proposed reformulated FAO–PTC framework, combined with appropriately selected discrete-time models, provides an effective balance between accuracy, model simplicity, and implementation practicality for real-time sensorless induction machine drives.