关键词: 经纱张力, 分数阶, 反步, 滑模控制, 神经网络

Abstract: With the progress of current computer technology and modern control methods and theories, the textile field has been fully developed in the past decade, gradually realizing intelligence and advancement. However, the domestic textile industry still has the problems of low competitiveness and high labor costs. Looms in textile machinery need  to be closely integrated with electromechanical equipment. High-quality looms apply more advanced algorithms to looms on the basis of continuous pursuit of higher weaving efficiency and fabric quality, reducing the degree of manual intervention. The performance of the let-off mechanism, a direct tension control mechanism, determines the speed and efficiency of the loom spindle. Studying the let-off system and developing a more efficient control algorithm or structure is an important factor to improve the performance of the loom, which meets the national economic needs and social significance of China.
In order to enhance the matching degree between the let-off mechanism and weaving requirements of looms, the key control algorithm of the let-off mechanism is designed, which is combined with modern control theory to improve the robustness and stability of the control algorithm. This research aims to develop a neural backstepping fractional order fast terminal sliding mode controller (RBF-BCFOFTSMC) to control the warp tension. Firstly, the time-varying mathematical model of the let-off system of the loom was established through dynamic analysis. In order to improve the dynamic response performance of the system and reduce chattering, a new backstepping fractional order fast terminal sliding mode control method was derived. Since there are disturbances such as motor vibration and heald frame motion in the weaving process of the loom, the upper bound of the total disturbance of the system is unknown, so an adaptive law was designed to estimate the upper bound of the external disturbance. The time-varying characteristics of the system make the controller have unmodeled and modeling uncertainties. The neural network parameter adaptive law was designed to approximate the real system state, and the Lyapunov stability was used to prove the rationality of the system. In order to verify the effectiveness of the designed control strategy, it was compared with traditional sliding mode control (SMC) and neural PID (RBFPID) in simulation experiments and actual working conditions. The results show that RBF-FOTSMC not only reduces chattering in warp tension control, but also has high robustness and response performance.
Through the research, the algorithm design and experiment of the let-off control system have been successfully completed, which has greatly improved the control effect, robustness and stability of the system. However, as the loom let-off system is a complex control system, more research needs to be supplemented in the future from two main points. First, it is necessary to study the influence of heald frame, weft insertion, beating up and other movements on loom tension, and analyze the influencing factors for corresponding tension compensation. Second, the adopted hardware needs to be optimized. If the controller with faster processing speed can be replaced, the high-speed and advanced level of the loom will be improved.

Key words: warp tension, fractional order, backstepping, sliding mode controller, neural network