Sensorless speed control of induction motor drives using reinforcement learning and self-tuning simplified fuzzy logic controller
Fuzzy 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 adap...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Institute Of Electrical And Electronics Engineers Inc.
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28455/2/00337271220241013541483.pdf http://eprints.utem.edu.my/id/eprint/28455/ https://ieeexplore.ieee.org/document/10614142/authors |
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| Summary: | Fuzzy 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. |
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