Abdullah, Q.Farah, N.Ahmed, M.S.Shah, N.S.M.Aydogdu, O.Talib, M.H.N.Al-Moliki, Y.M.2024-09-222024-09-2220242169-3536https://doi.org/10.1109/ACCESS.2024.3435529https://hdl.handle.net/20.500.13091/6282Fuzzy logic controls (FLCs) have emerged as a promising solution for speed regulation in induction motor (IM) drives, offering adaptability to non-linearities, parameter variations, and external disturbances. However, conventional FLCs with fixed parameters and a huge number of rules can limit adaptiveness and increase system complexity, leading to deteriorated performance and high computational requirements. Moreover, reliance on costly encoders in traditional sensor-based IM drives introduces measurement errors and contributes toward the overall cost. To tackle these challenges, this paper proposes an integrated sensorless IM drive with a simplified self-tuning FLC (ST-FLC) and data-driven reinforcement learning (RL) for speed estimation. By employing a simplified 9-rule FLC instead of an intensive 49-rule counterpart and integrating a simple self-tuning mechanism based on mathematical equations, adaptiveness is maintained while computational overhead is reduced. Furthermore, the adoption of RL-based sensorless speed estimation eliminates reliance on encoder data, offering a cost-effective and computationally efficient alternative. Unlike conventional sensorless methods, the proposed sensorless-RL approach is data-driven and does not rely on motor parameters, leveraging a pre-trained policy for efficient speed estimation. Validation through simulation and experimentation on the dSPACE DS1104 platform demonstrates the efficacy of the proposed ST-FLC Sim 9-rule with sensorless RL. The method showcases accurate speed estimation, with simulation results comparable to standard 49-rule FLC and superior experimental performance. Significant computational time reduction is achieved with the proposed approach, resulting in a notable improvement in experimental performance metrics. Specifically, reductions of 50.5%, 20.4%, 15%, and 14.9% in settling time, current ripples, torque ripples, and current harmonics, respectively, underscore the practical benefits of the proposed integrated ST-FLC Sim 9-rule with sensorless-RL IM drive system. Authorseninfo:eu-repo/semantics/openAccessArtificial neural networks; computation requirement; Estimation; FLC; Motors; Observers; RL; Rotors; sensorless IM drives; simplified rules; ST-FLC; Stators; TorqueComputer circuits; Cost effectiveness; Digital storage; Fuzzy logic; Induction motors; Neural networks; Parameter estimation; Sensorless control; Signal encoding; Computation requirement; Fuzzy logic control; Induction motor drive; Observer; Reinforcement learnings; Self-tuning FLC; Selftuning; Sensorless; Sensorless induction motor drives; Simplified rule; Reinforcement learningArticle10.1109/ACCESS.2024.34355292-s2.0-85200222084