Production scheduling in a knitting workshop is an important method to improve production efficiency, reduce costs and improve service. In order to achieve a reasonable allocation of parallel machines as well as cooperation between different machines within the workshop, thereby ensuring the optimal scheduling of production plans, this paper proposes a scheduling method using an improved genetic algorithm (IGA) based on tabu search. Firstly, the production scheduling model of a knitting workshop is established. Secondly, an IGA based on the minimum processing time rule, the priority idle machine rule and the production order ranking code is used to optimize the solution. Finally, an experiment platform for knitting workshop production is built to verify the proposed scheduling method. The experimental results show that the proposed IGA based on tabu search performs well in terms of preconvergence speed, global search capability and local search capability. The IGA converges faster than the traditional genetic algorithm by about 25%, reduces the redundancy time of scheduling, meets the production requirements of the knitting intelligent workshop and has a good reference value for promoting the intelligent development of knitting production.

During the operation of the knitting underwear machine, unstable yarn tension can have adverse effects on the quality of the underwear fabric, such as uneven density and reduced resilience, which significantly diminishes the comfort and durability of the textile products. In response to the issues of slow tension response speed and low tension control accuracy in the traditional yarn feeding method of current knitting underwear machines, a tension control system has been designed for the yarn feeding process. The system employs an adaptive ADRC algorithm, which is based on fuzzy sliding mode control, to detect the real-time trend of yarn tension changes, adjust the real-time target speed of the yarn feeding motor, and then utilizes permanent magnet synchronous motor vector control technology to drive the motor operation. This reduces tension disturbances during the yarn feeding process, enhances the response speed during sudden tension changes, and achieves precise control of yarn tension. Finally, a tension controllable yarn feeding experimental platform was constructed to simulate the knitting underwear machine, and tests were conducted to evaluate the control effectiveness of the yarn feeding tension control system under various algorithm controls. The results show that the control system based on the adaptive ADRC algorithm reduces the amplitude of tension fluctuation in the yarn target tension stabilization phase by more than 33% compared to traditional PID and ADRC algorithms. The average response time is shortened by over 35%, respectively. These comparative data demonstrate that the system can enhance the stability of yarn tension control during the yarn feeding process while meeting the varying elasticity demands of different fabrics and the specific yarn feeding speed requirements of various knitting processes, ultimately ensuring fabric quality.

To address the issue of mechanical resonance frequency detection in dual-inertia servo systems, this paper proposes an online identification method for mechanical resonance frequency using a low-pass filter and cascaded second-order generalized integrator—frequency-locked loop (LPF-CSOGI-FLL). Initially, the cascaded second-order generalized integrator—frequency-locked loop (CSOGI-FLL) is employed to eliminate the interference of direct current (DC) bias in resonance frequency identification. From a dual-stage structural perspective, the first second-order generalized integrator (SOGI-FLL) acts as a band-pass pre-filter to extract the mechanical resonance signal from the signal to be tested. The second SOGI-FLL generates a signal with equal amplitude and frequency to the mechanical resonance and obtains the frequency of the resonance signal through the frequency-locked loop. Subsequently, a low-pass filter (LPF) is applied to the frequency feedback loop of the second-stage SOGI-FLL, effectively reducing the oscillation of the estimated frequency. Finally, combining the CSOGI-FLL with an LPF forms a novel structure, namely, LPF-CSOGI-FLL. The results demonstrate that the proposed method significantly improves the detection accuracy of mechanical resonance frequency under various conditions. Compared to traditional offline techniques, this method overcomes the impact of resonance frequency drift and enhances system stability.

To solve the problems of poor real-time tension tracking, large overshoot, long adjustment time, and large tension fluctuation error that exist in the current control system for yarn feeding in the circular weft knitting machine, this paper presents an optimized and improved adaptive genetic algorithm (AGA) for adjusting the PID (Proportional Integral Derivative) parameters of the tension control part of the yarn-conveying system (AGA-PID). The algorithm is combined with the field-oriented control technology of the permanent magnet synchronous motor to design a closed-loop control strategy for the yarn-conveying system of a circular weft knitting machine. The MATLAB-Simulink simulation model was used to analyze the external tension loop-based AGA-PID controller and permanent magnet synchronous motor dual closed-loop control strategy of the yarn transport control system. The experimental verification was conducted by constructing an experimental platform that simulates the real process of yarn transportation. The outcomes of system simulations and experiments have consistently demonstrated that the AGA-PID control algorithm outperforms the GA-PID and PID control algorithms in terms of overshooting, response speed, stability, and anti-jamming ability. The yarn transport control system, which is designed to operate based on an AGA-PID control algorithm, is capable of reducing the fluctuations in tension that occur during the transportation of yarn and exhibits enhanced control accuracy, effectively stabilizing yarn tension control during the conveying process. The application of this algorithm can enhance the overall elasticity uniformity and surface flatness of knitted fabrics, thereby facilitating the realization of adjustable and controllable local elasticity in fabrics.