Advanced Control Strategies for High-Performance Induction Motor Drives: An Integrated, Application-Oriented Survey

Abstract

Induction motors remain the workhorse of modern industry thanks to their robustness, cost effectiveness and high efficiency, but the growing demands of electrified transport, high-performance automation and Industry 4.0 impose increasingly stringent control requirements. This paper presents an integrated, application-oriented survey of control strategies for high-performance induction motor drives, covering classic scalar V/f control as a baseline and advanced field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC), nonlinear/robust schemes and intelligent/data-driven and digital twin-assisted solutions. The methods are analyzed within a unified framework in terms of dynamic response, torque and flux ripple, current harmonic distortion, efficiency, robustness, implementation complexity and suitability for sensorless and fault-tolerant operation. Emphasis is placed on hybrid strategies that combine classical vector or DTC structures with MPC, fuzzy and neuro-fuzzy logic, neural network-based observers, reinforcement learning and digital twin-enabled monitoring to reconcile fast dynamics with high efficiency, low ripple and lifecycle reliability. Consolidated comparison tables and a hybrid control map highlight typical performance trends, trade-offs between simplicity and performance, and the complementary roles of AI and digital twins as system-level enablers. The survey also outlines promising research directions toward systematically designed hybrid controllers, lightweight digital twins for embedded platforms and experimentally validated benchmarks that can accelerate the industrial uptake of next-generation induction motor drives.