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Optimized fuzzy logic controller cuts average robotic arm tracking error by 89% in simulations
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A genetically optimized fuzzy logic controller for robotic arms is described in the International Journal of Automation and Control. The system improves accuracy and resilience in simulations of a three-degree-of-freedom robotic arm. Moreover, it outperforms alternative control systems when the device is exposed to disturbances and changes in the system parameters.
Source:https://techxplore.com/news/2026-11-optimized-fuzzy-logic-average-robotic.htmlPreviousImproving community readiness for evacuations with electric vehiclesNextNetflix’s AI Recommendations Aim to Play Matchmaker to Your Tastes
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