Robust Adaptive Control Strategies for AI-Enabled Industrial Robotic Work Cells under Manufacturing Variability
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Generally, conventional adaptive approaches show convergence durations incompatible with industrial cycles, and traditional model predictive control frameworks deteriorate under model uncertainty. Thus, this study examined robust adaptive control strategies for AI-enabled industrial robotic work cells under manufacturing variability. The study adopts the Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) framework for data collection. Using a set of inclusion and exclusion criteria, secondary data were sought from six (6) databases using relevant search terms. A total of seventeen (17) studies were selected for the study. The findings showed that robust strategies used in AI-enabled industrial work cells include improved responsiveness, precision, flexibility, and operational resilience under changing manufacturing conditions. Results showed that the types of manufacturing variability addressed by robust adaptive control approaches in industrial robotic systems generally relate to operational uncertainty, environmental disturbances, process fluctuations, dynamic scheduling disruptions, equipment faults, and changing production requirements. Findings showed that the techniques employed in robust adaptive control for AI-enabled industrial robotic work cells integrate artificial intelligence, machine learning, adaptive feedback systems, sensor fusion, and predictive optimization mechanisms. Despite the challenges, it was revealed that robust adaptive control strategies improve stability, reliability, operational efficiency, precision, responsiveness, and overall manufacturing performance in AI-enabled industrial robotic work cells. The study concluded that robust adaptive control strategies are employed by industrial robotic work cells, and that AI-enabled approaches enhance productivity. It is recommended that industries should invest in intelligent sensing systems, real time monitoring technologies, and adaptive feedback mechanisms to improve production flexibility under dynamic manufacturing conditions.
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