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Optimized fuzzy logic controller cuts average robotic arm tracking error by 89% in simulations

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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.

The researchers compared fuzzy logic control, an adaptive neuro-fuzzy inference system (ANFIS) and a genetic algorithm–optimized fuzzy controller (GA-FLC). Genetic algorithms are optimization systems inspired by natural selection and are often used to find the best solution to complex problems by "evolving" candidate parameters until an optimal fit emerges.

The team carried out simulations and showed that GA-FLC reduced average root mean square errors by 89% compared with conventional feedback control. The system thus improved on the alignment between the robot's intended and actual movements. GA-FLC also maintained stable performance even when significant changes were made to the system parameters, while earlier approaches failed under such severe variations.

The findings could have implications for industrial automation, where robotic arms perform precision tasks such as assembly, welding and material handling, the work suggests. Even greater resilience to disturbances could improve reliability and reduce errors in manufacturing. The next step will be to undertake hardware implementation and real-time testing as proof of principle in a test industrial setting.

, GA-optimised fuzzy control for trajectory tracking of a 3-DOF robotic manipulator, International Journal of Automation and Control (2026).


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