Biomechanical data–driven fuzzy control for enhancing PLC automation education

The purpose of this study is to employ fuzzy control theory along with biomechanical information in the PLC automatic control curriculum of vocational education. The paper uses a large dataset that includes biomechanical measurements of muscle force exertion, joint angle, and muscle activation, which were collected from a vocational school using fuzzy control algorithm specifications. The methodology includes data gathering and preprocessing, fuzzy control algorithm development, integration with PLC system, simulation, and validation of results. It is a well-structured algorithm that combines inference rules and fuzzy sets and membership functions. For real-time synchronisation, biomechanical information is transformed into fuzzy inputs, and the addition process is performed using the algorithm within the PLC environment. The experiments are conclusive in demonstrating that the system is more accurate, faster in response time, and more stable than traditional PLC control techniques. These findings validate the promise of the approach to improve the efficiency and user satisfaction of PLC automation courses with a virtuous basis for further exploration. The key contribution of this research is applying fuzzy reasoning to real-time biomechanical data to enhance PLC teaching and control, enabling more engaging vocational education and improved system responsiveness.