Abstract
Tension is a key processing parameter in the process of composite fiber tape winding. The fluctuation of tension will affect the accuracy of winding and the performance of winding products, such as stress uniformity, fatigue resistance, strength, compactness and resin content. In view of the winding tension is time-varying and the application of tension needs to be more accurate, a tension calculation model is established. Due to the influence of dynamic performance of tension control system by cogging torque, inverter dead zone, ring gear clearance, friction torque, parameter drift, and measurement noise, an adaptive sliding mode control (ASMC) based on extended state observer (ESO) is proposed. The stability of the closed-loop system is proven by Lyapunov theory, the system state variables is estimated by ESO, and the input dead-zone is compensated by the designed adaptive law. Simulation and experimental results show that ESO-based ASMC improves the robustness and dynamic response performance of the tension control system, and can effectively suppress the chattering of the sliding mode control system. The void content and residual stress of composite products have been improved obviously.
Highlights
Due to the rapid development of military and civil industries, especially advanced technologies such as aerospace and aviation, some materials with superior performance are needed
Compared PID, sgn, sat(0.02) and sat(0.05), control error of the design extended state observer (ESO)-based adaptive sliding mode control (ASMC) is reduced by 78.6%, 50.5%, 59.1% and 65.9%, respectively
According to the tension calculation model, it can be known that the application of tension value is time-varying in the winding process
Summary
Due to the rapid development of military and civil industries, especially advanced technologies such as aerospace and aviation, some materials with superior performance are needed. In literature [10], a robust control algorithm based on disturbance observer for tension control system of cylinder winding equipment is proposed. In literature [14], for multiparameter winding control system, a sliding mode feedback linearization control algorithm is designed. In literature [16], an observer based feedback control method is proposed to reduce the increase of tension. In literature [33], combining internal model control and inverse system method is proposed, which can effectively improve the dynamic performance and control accuracy of bearingless permanent magnet synchronous motor. In literature [34], a speed observation based on artificial neural network inverse method is proposed, which can reduce the influence of speed detection on the accuracy and stability of bearingless induction motor system. The simulation and experiment show that the designed algorithm is reliable and effective, and winding products are more superior in performance
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