Magnetically coupled rodless cylinder, Nonlinear model, Position control, Radial basis function neural network (RBF-NN), Neural network (NN), Ocean engineering, TC1501-1800, Mechanical engineering and machinery, TJ1-1570

Abstract Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms, electrostatic paintings, and other industrial applications. However, they exhibit strong nonlinear characteristics, which lead to low servo control accuracy. In this study, a mass-flow equation through the valve port was derived to improve the control performance, considering the characteristics of the dynamics and throttle-hole flow. Subsequently, a friction model combining static, viscous, and Coulomb friction with a zero-velocity interval was proposed. In addition, energy and dynamic models were set for the experimental investigation of the magnetically coupled rodless cylinder. A nonlinear mathematical model for the position of the magnetically coupled rodless cylinder was proposed. An incremental PID controller was designed for the magnetically coupled rodless cylinder to control this system, and the PID parameters were adjusted online using RBF neural network. The response results of the PID parameters based on the RBF neural network were compared with those of the traditional incremental PID control, which proved the superiority of the optimization control algorithm of the incremental PID parameters based on the RBF neural network servo control system. The experimental results of this model were compared with the simulation results. The average error between the established model and the actual system was 0.005175054 (m), which was approximately 2.588% of the total travel length, demonstrating the accuracy of the theoretical model.