To solve the position control problem of the induction motor with parameter perturbations, load disturbances, and modeling errors, a predefined-time position tracking optimization control method with prescribed performance is proposed. In practice, the rotor flux linkage of the induction motor can't be measured, and a predefined-time sliding mode observer (PTSMO) is applied to accurately estimate it. Additionally, predefined-time disturbance observers (PTDOs) are employed to identify the uncertainties in the motor system. The position and flux linkage controllers are then designed by integrating the predefined-time control approach with the prescribed performance function method, realizing accurate tracking control of the induction motor within a predefined time. Next, the adaptive genetic algorithm (AGA) and the improved particle swarm optimization (IPSO) technique are combined to optimize the designed controllers, enhancing the convergence rate and steady-state accuracy of the induction motor. Finally, comparative analyses through simulations and dSPACE simulated experiments validate the efficacy of the proposed control method, highlighting its applicability in practical motor systems.
物联网设备之间通过不同的通信协议进行数据传输与信息交互.针对设备异构通信协议相互理解问题,深入研究了多工业协议解析技术,研究并实现了一种可支持协议扩展的语义解析网关.网关定义了多种工业协议解析脚本、开发了词法分析和语法分析程序、生成对应的协议解析指令集和实现了解析脚本执行程序和解析数据输出,建立了整体的语义解析系统.网关通过定义不同协议的解析脚本对不同协议的数据报文进行语义解析,析出的语义数据可以直接被设备或者云端应用,从而实现了更加灵活的数据传输和信息交互.网关通过扩展解析脚本来支持对协议扩展,不需要针对每种协议单独设计解析程序,极大地减少了协议解析程序开发的难度和工作量,更符合当前及未来物联网发展的趋势.通过对网关性能进行可靠性和实时性测试实验,验证了网关对工业协议解析转换稳定可靠及时.
In the context of sharing economy, establishing a stable alliance is crucial in the logistics service industry. Consequently, the challenge of ensuring fair profit allocation arises within the coalition of logistics enterprises. However, due to the presence of incomplete information in the coalition, some traditional point-valued solutions of cooperative games, such as the Shapley value, may be inadequate. These solutions are more proper for cooperative games where accurate estimate of both the general profit of the alliance and the participation level are feasible. In this study, we address the issue of profit allocation for logistics enterprise coalitions with incomplete information and propose a relevant profit allocation model. To demonstrate the applicability of the proposed model, a case study is provided. The results show that the fuzzy Shapley value significantly enhances the multi-party cooperation and can serves as an effective tool for the fair and equitable allocation of profits within logistics enterprise coalitions.
Aiming at the weak generalization ability of traditional rolling bearing fault diagnosis methods, a rolling bearing diagnosis method based on dynamic convolutional capsule networks (DC-CapNets) was proposed. First, one-dimensional vibration signals are preprocessed and divided into a training set and a test set. Then the fast Fourier transform (FFT) is used to convert the training set and the test set. In this model, two dynamic convolution layers and pooling layers are used to extract the features of the input frequency signals, and the feature information is transmitted to the capsule network to obtain the diagnosis results. The CWRU (Western Reserve University) data set validation shows that the proposed method still has high fault diagnosis accuracy under noise and variable load conditions, and has good anti-noise and generalization performance, which is superior to the convolutional neural network (CNN) and deep neural network (DNN).
Addressing the challenge of rehearsing large-scale equipment assembly, particularly for oversized components like wind turbine towers, hubs, nacelles, and blades, which often face quality issues such as substandard workmanship and wide tolerances during on-site assembly, a system has been developed for simulating wind turbine assembly. This system enables digital wind turbine assembly by creating virtual and production process models and employing intelligent database analysis. It resolves the problem of the absence of pre-production for large-scale equipment, meets the batch production needs of the wind turbine manufacturing industry, enhances the safety of wind turbine operations, improves operator assembly skills, and boosts production efficiency. This platform has already been implemented in the wind turbine manufacturing industry, yielding significant economic benefits.
