An ADRC Parameters Self-Tuning Control Strategy of Tension System Based on RBF Neural Network

Abstract

High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll precision coating machine. However, the complex relationships in tension system make the problems of decoupling control difficult to be solved, which has limited the improvement of tension control accuracy for the coating machine. Therefore, an ADRC parameters self-tuning decoupling strategy based on RBF neural network is proposed to improve the control accuracy of tension system in this paper. Firstly, a global coupling nonlinear model of the tension system is established according to the composition of the coating machine, and the global coupling model is linearized based on the first-order Taylor formula. Secondly, according to the linear model of the tension system, a parameters self-tuning decoupling algorithm of the tension system is proposed by integrating feedforward control, ADRC and RBF. Finally, the simulation results show that the proposed tension control strategy has good decoupling control performance and effectively improves the tension control accuracy for the coating machine.

Linear Active Disturbance Rejection Lateral Controller for Unmanned All-Terrain Vehicle

“…At present, for the horizontal control of automatic driving, scholars mostly adopt PID control strategy [5,6], mainly using its ability not to depend on the precise system model. However, reasonable selection and selfadaptation of PID model parameters have become difficult [7]. As a variant of PID control, the pure tracking strategy [8] solves the problem of control parameter design and was applied by Carnegie Mellon University to Navlab2V unmanned vehicles [9].…”