Developing intelligent control models for permanent magnet synchronous motors (PMSM) is crucial from both a theoretical and practical standpoint due to the growing use of these motors in industrial production, agricul...
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Developing intelligent control models for permanent magnet synchronous motors (PMSM) is crucial from both a theoretical and practical standpoint due to the growing use of these motors in industrial production, agriculture, and edge computing applications. The standard proportional integral derivative (PID) control is inadequate for Edge of Things (EoT) applications with high requirements and demands when operating a PMSM because of its poor anti-disturbance capacity. To increase the robustness of a PMSM, this study proposes a controller that integrates the existing synovial control method with our newly introduced sliding mode control-based fuzzy control theory. Furthermore, we compare the performance of synovial and fuzzy sliding mode control with the conventional PID control system for the PMSM control system. To evaluate the control strategies proposed in this study in terms of different performance measures such as robustness, dynamic steady-state control, and stability, we performed multiple experiments. The experimental results illustrate that intelligent control strategies have better resilience and performance as compared to the PID control method, making them more suitable for edge computing applications. Further, this study also provides insight into the potential of intelligent control algorithms for improving the performance of PMSM in an EoT computing context.
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure that y...
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We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure that yield compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a weighted graph representation of the input dataset. Such a weighted graph representation is induced by a specific parameter configuration of the dissimilarity measure adopted by an agent for the search. During its lifetime, each agent evaluates different parameter configurations and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters. Moreover, we demonstrate that our algorithm shows comparable performances with other similar state-of-the-art algorithms when facing specific clustering problems. Notably, we compare our method with respect to several graph-based clustering algorithms and a well-known subspace search method.
This article presents a new methodology using distributed algorithms to identify and prevent errors in production and service. A sequential production/service line is selected to challenge the analysis, and reveal if ...
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This article presents a new methodology using distributed algorithms to identify and prevent errors in production and service. A sequential production/service line is selected to challenge the analysis, and reveal if the distributed algorithms can outperform centralized algorithms in automating error prevention. agent-based error prevention algorithms (AEPAs) are developed for distributed agents to identify and prevent errors with decision rules. Analytical studies and simulation experiments are conducted to compare AEPAs with traditional centralized error prediction and detection algorithms. The results show that the AEPAs employing nominal and optimistic rules perform better than the centralized algorithms in terms of preventability and reliability. Collaboration among agents improves AEPAs' performance. It is recommended to prevent errors by two agents simultaneously executing the AEPA employing the integrated nominal rule. (C) 2011 Elsevier Ltd. All rights reserved.
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