Neural Network Based RISE Control of Winding Tension

doi:10.3901/JME.2021.24.074

Abstract

Abstract: Filament winding system is a typical nonlinear system. Tension control accuracy of the winding process determines the quality of the winding product. However, the nonlinear characteristics, friction and external interference of the system severely restrict the improvement of the tension control performance of the winding process. At present, the existing methods are mainly based on the synchronous control of the rewinding and unwinding axes, and the tension control research is carried out through complex operations such as decoupling. The calculation is large and is not conducive to the application of control algorithms. In order to accurately describe the tension generation mechanism and actual friction characteristics of the winding system, a uncomplicated nonlinear mathematical model of the winding system is established. Meanwhile, taking adaptive method as the neural network weights training procedure and the approximation function of the disturbance based on the adaptive neural network is designed. Therefore, the disturbance can be compensated at the control law of the continuous robust integral of the sign of the error (RISE) controller. Based on the Lyapunov stability theory, the asymptotic stability of the system is proved. Finally, a comparison verification example between the proposed controller and the existing methods is given. Results show that the proposed method of RISE controller based on adaptive neural network disturbance compensation significantly enhances the system's ability to suppress external disturbances, and improves the system control accuracy.

Key words: winding tension, tension control, neural networks, disturbance compensation, RISE, parameter adaptive

CLC Number:

TP137

MI Junjie, YAO Jianyong, DENG Wenxiang. Neural Network Based RISE Control of Winding Tension[J]. Journal of Mechanical Engineering, 2021, 57(24): 74-82.

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