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Abstract In the context of Industry 4.0, which prioritizes intelligent and efficient solutions for industrial systems, this paper introduces an innovative methodology for fault detection and fault-tolerant control of DC motors. Leveraging the capabilities of machine learning and reinforcement learning, our approach aims to achieve optimal performance while maintaining a low computational burden. At the heart of our strategy lies a reinforcement learning-enhanced proportional-integral controller meticulously designed for precise positioning of DC motors. Through extensive comparative analysis, we establish the superiority of this controller in terms of precision, efficiency, and user accessibility when compared to traditional techniques. To ensure robust fault detection, we synergize a model-based observer with Mahalanobis distance-based outlier analysis, creating a swift and accurate diagnostic method for sensor faults. In cases of sensor malfunctions, an internal model-based control strategy comes into play, enabling the system to uphold its effectiveness despite disruptions. The effectiveness of our proposed methods is vividly demonstrated through simulations in the MATLAB environment, utilizing a DC motor subjected to sensor failures. The results unequivocally highlight the advantages of our approach, showcasing improved precision, faster operation, cost-effectiveness, and streamlined simplicity. As such, our approach finds suitability for industrial applications. In our quest to strike a delicate balance between performance and complexity, our techniques are purposefully crafted to provide intelligent yet pragmatic solutions that promote reliability, safety, and sustainability. This paper contributes to the evolving landscape of intelligent industrial solutions by offering a comprehensive framework that optimizes performance while minimizing complexity and costs. In doing so, we lay the foundation for a more efficient and resilient industrial ecosystem.
Sardashti et al. (Sat,) studied this question.