Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

Rapier looms are currently important equipment in the weaving process. The control system of the loom determines the performance of the loom, to a large extent. To effectively reduce the production cost and energy consumption and to improve the start-up performance and production efficiency of rapier looms in industrial production, this paper develops an integrated rapier loom control system based on the direct drive of a switched reluctance motor (SRM) spindle and conducts field tests and applications. The contribution and innovation of this paper is to develop a complete set of low-cost control systems, propose an SRM single neuron fuzzy PID speed control strategy based on voltage chopping control and use it for the control of the main shaft drive technology of rapier looms. The integrated rapier loom control system based on the SRM spindle direct drive proposed in this paper reduces the production costs and energy consumption and improves the start-up performance and production efficiency of the rapier loom. This text carries on the systematic plan design to the control system from the hardware system and the software system. First, according to actual needs, starting from the aspect of reducing control costs and combined with the characteristics of embedded systems, such as tailorability, low cost, and strong scalability, this paper proposes a control system hardware structure based on CAN bus communication and a fully embedded STM32. The control system is divided into multiple control modules, such as the main control module, the spindle drive module, and the power transmission coil module. The system conducts a distributed control to the loom through the CAN bus and is equipped with various communication interfaces, such as Ethernet and RS485. Second, combining the characteristics of the SRM with a simple structure, a large starting torque and the operation mode of the loom, the basic control mode of SRM voltage chopping control is determined. To improve the efficiency and start-up performance of the speed control system, the SRM single neuron PID control algorithm is proposed, and a single neuron is used to improve the PID parameters. On this basis, fuzzy control is introduced to adjust the output gain of a single neuron PID control online to improve the system performance and reduce system energy consumption. Finally, the entire set of rapier loom control systems was verified, tested and debugged on site. The results show that each functional circuit works normally, and that the designed control system can meet the speed response demand of the loom at 850 rpm and reduce the production cost and energy consumption. The comparison experiment between the single neuron fuzzy PID algorithm of the motor and the traditional PID control algorithm in the actual loom production process proves that the proposed control algorithm has a better dynamic response performance. The proposed control algorithm effectively improves the starting performance and production efficiency of rapier looms and meets the actual needs of industrial applications.

This paper investigates the warp let-off and take-up mechanism of rapier looms to solve the problem that the warp tension of rapier looms fluctuates greatly and the warp let-off is difficult to maintain constant. The design and hardware implementation of a let-off and take-up control system based on fuzzy neural network (FNN) and vector control (VC) are presented to improve the control level of warp tension and drive performance of the let-off and take-up system. Firstly, the spring-damper dynamic model of the warp is established according to the mechanical properties. The parametric expression of warp tension and the control strategy of fixed angle interval based on let-off and take-up motions are constructed according to the generation mechanism and fluctuation law of warp tension. Then, based on fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). In addition, a specialized let-off and take-up hardware circuit is constructed through embedded technology, and the SVPWM algorithm is used as the driving scheme of the hardware circuit. Finally, simulation and actual weaving experiments test the proposed let-off and take-up control system and hardware circuit. The results show that, compared to PID and fuzzy PID, the proposed fuzzy neural network algorithm has higher tension control accuracy and can effectively restrain the rapier loom’s warp tension undulation. The designed hardware circuit and SVPWM algorithm have a fast and stable driving ability, which ensures the constant let-off amount.

Accurate estimation of the state of charge (SOC) in lithium batteries is crucial for optimizing energy utilization and ensuring battery safety within battery management systems (BMSs). While deep learning techniques have made significant progress, time-series models based on the gate recurrent unit (GRU) have gained widespread application in SOC estimation. However, their performance heavily hinges on the initial hyperparameter settings, impacting both precision and application range. To address this challenge, we propose a novel algorithm—the logistic-tent-gold subtraction average-based optimizer (LTG-SABO)—which combines composite chaotic mapping with the golden sine algorithm. The LTG-SABO algorithm aims to optimize key hyperparameters of the GRU model, thereby enhancing precision and robustness in SOC estimation. By leveraging the Logistic-tent composite chaotic mapping for population initialization, our approach not only expands the search space but also effectively prevents algorithm convergence to local optima. Additionally, integrating the Gold-SA strategy further enhances the global search capability of the SABO algorithm, significantly reducing convergence time. The computational results reveal that the proposed LTG-SABO-GRU model outperforms the traditional GRU model in estimating SOC precision under both normal and extreme temperature conditions. Specifically, the root mean square error and mean absolute error show a substantial improvement, increasing by over 50% compared to the traditional model. Moreover, the LTG-SABO-GRU model exhibits significantly fewer convergence iterations than existing typical population optimization algorithms. This study introduces a novel, efficient, and practical approach for SOC estimation in BMS applications.

At present, the fault diagnosis methods of lithium battery pole rolling mill mostly rely on manual experience and the self-test function of mature control devices such as frequency converters and lack the ability of intelligent fault diagnosis for the whole equipment and the ability to evaluate the health state of the equipment during operation. To improve the intellectual health diagnosis ability of lithium battery pole double rolling mill equipment, starting from the structure and technology of lithium battery pole double rolling equipment, this paper analyzes its common fault types. It summarizes the shortcomings and common fault types of existing equipment. Then, we introduce fuzzy reasoning into the fault diagnosis method based on Expert Systems and establish the FEFDM of lithium battery pole double rolling equipment. Finally, we introduce the concept of health degree, effectively connect BP neural network and health degree through the fuzzy set, and establish an equipment operation health state evaluation method based on an improved BP Neural Network, which realizes the evaluation ability of the health state of double roller equipment. In addition, we use Extended Kalman Filtering (EKF) to clean the “dirty data” and filter out the Gaussian white noise from the signal. The health diagnosis method proposed in this paper can meet the ability to accurately locate and diagnose the fault of lithium battery pole double roller equipment and evaluate the health state of equipment operation and maintain the equipment in advance.

Tension control is very important for the intelligent control system of rapier looms because stable tension guarantees a tight fabric structure, moderate elasticity and good forming. To solve the problems of low-tension measurement accuracy of the existing rapier loom, the complex structure of the tension control strategy and algorithm, and the high research and development cost, this paper proposes new research from the perspective of high-precision and nonlinear processing of tension detection signals. The median filtering and limiting filtering algorithms are integrated to solve the uncertainty problem caused by the disturbance and fluctuation of the tension signal, and an excellent sample dataset is obtained. The attenuation factor and the number of learning times are introduced to design and adjust the learning rate of the back propagation neural network algorithm. In addition, the overfitting problem of the backpropagation neural network model in the current research process is solved. The experimental simulation results show that the tension detection fluctuation range of this method is 0.8 kg, and the tension detection error is within 0.1%. On the basis of the existing tension control algorithm, the tension detection accuracy is improved, which presents a new research perspective for the wide application of high-precision tension control strategies in rapier looms. It is of great importance to improve the production efficiency and fabric quality of rapier looms.