Dynamic Control Method of Improving Extreme Learning Machine Algorithm in Wood Spinning Process

Dynamic Control Method of Improving Extreme Learning Machine Algorithm in Wood Spinning Process

The precise control of the feed system of the rotary cutting machine is the key to achieving uniform thickness rotary cutting of wood on the log-core veneer lathe. The application of Improving the Extreme Learning Machine Model in the feed system can effectively solve the problem of unstable feed rate matching in traditional control methods. This study analyzed the working mechanism of log-core veneer lathe and established a kinematic model of its feed system. Using the thickness and thickness variation of the wooden board as the control results of Improving the Extreme Learning Machine Model, in order to solve the optimal weight and threshold of Improving the Extreme Learning Machine Model, this paper uses an improved particle swarm optimization algorithm to solve. Finally, in the Matlab software environment, log-core veneer lathe motion model and control model are written, and simulation experiments are conducted to verify. The results show that Improving the Extreme Learning Machine Model effectively improves the control performance of the system, with stable feed rate changes, good real-time performance, fast convergence, high control accuracy, and strong adaptability.