Adaptive fuzzy observer and control in servo drivers for two-axis drilling machine

Drilling technology is a crucial manufacturing process in the application of servo drive control systems in the machining industry. Traditional position and velocity control can easily lead to repeated processing or damage to the drill bit. In this study, a method was proposed for achieving two-axis drilling control by combining torque and velocity estimation. The classical Luenberger observer is employed for measuring torque and velocity feedback. However, it cannot effectively solve the issue of encoder feedback quantization, and its performance is suboptimal for nonlinear systems. Therefore, we propose an adaptive fuzzy observer method to improve the Luenberger observer's adaptability and control robustness to nonlinear systems. This technology helps with the estimation and control of velocity and torque in practical drilling platforms. Simultaneously, an adaptive fuzzy observer is employed for the feed motor to enhance positioning accuracy. The primary contribution of this study is the effective estimation of torque and velocity using adaptive fuzzy observer technology. It also improves drilling efficiency and precision through torque control and velocity control while reducing the cost and complexity of the control system. The research shows that, compared to traditional drilling technology, the adaptive fuzzy observer control method can cut costs and enhance machining accuracy, and compared with traditional driving and control methods, it can meet 8.7% energy saving requirement.