Model-based predictive brake control during weft insertion in air-jet weaving

Air-jet weaving is one of the most efficient manufacturing processes for producing textile fabrics. During weft insertion, a yarn brake influences the quality of textile fabrics significantly. This brake is set up manually by a machine operator and the duration of the set-up process depends essentially on his experience. Furthermore, an inappropriate braking process might induce high tension in the weft thread which could cause backward movements of the thread as well as a defect in the fabric. For this reason, a braking system is developed which consists of a camera-based sensor and a continuously adjustable brake. The camera-based sensor enables the estimation of the weft velocity which is then controlled by a Model-based Predictive Controller (MPC). In this contribution, a weft insertion model is derived and validated experimentally. After that, this model is reduced in order to ensure real-time capability for its application in a Kalman filter as well as in the MPC. Finally, an MPC based on piecewise linearisation is proposed and implemented on an air-jet weaving machine. The presented experimental results show that yarn braking system compensates the changing behaviour of weft threads in different machine cycles and brakes the weft thread appropriately without a backward movement.