Permanent manet synchronous motor(PMSM) has many advantages in the motor driving system of electrical vehicle because of its excellent performances,such as small cubage,great power density and high efficiency.This pa-per describes a novel flux-weakening control method for the PMSM.It is implemented based on the outer voltage regu-lation loop which regulates the phase angle of current leading EMF in order to prevent the saturation and uncontrol-lable status of the current regulator.Based on the d,q mathematical model of the PMSM,this dissertation ana-lyzes the principle of the flux-weakening,then a new method which called current lead angle flux-weakening control for PMSM based on PI current regulator is presented.Furthermore,in this thesis the air-gap flux orientation for flux-weak-ening control is analyzed and a block diagram of the control system is given.The simulation results in-dicate the fea-sibility and validity of this scheme.
The demodulation of fiber Bragg grating directly affects the measurement result of the sensor, and its parameters are easily disturbed by the environment and noise, thus causing the nonlinear error to reduce the demodulation precision. For the above problems, a tunable f-p filter fiber grating demodulation system with reference channel is designed in this paper. A fiber grating demodulation signal filtering method with limited sampling signal amplitude is proposed. Studied the common peak algorithm adaptability, by adjusting the signal-to-noise ratio(SNR) and wavelength signal sampling resolution two parameter test the algorithm performance, measured by experiment gaussian polynomial fitting for peak error in the SNR decreases remains within 1 pm, and poor in wavelength resolution when still precision is higher than other algorithms, suitable for the wavelength signal demodulation.
Accidents involving hazardous gas leakage from chemical plants occur frequently, highlighting the critical importance of accurately locating air pollution sources. Rapid localization of pollution sources through the collaborative efforts of multiple unmanned aerial vehicles (UAVs) can help prevent major disasters. To address this, this paper presents an air pollution source tracking algorithm based on an improved particle swarm optimization (IPSO) algorithm. By enhancing the inertia weight coefficient, the search efficiency and accuracy of the UAV swarm are significantly improved. The simulation environment considers a Gaussian plume model, which incorporates various atmospheric constraints such as temperature and wind speed, for UAV swarm navigation. The simulation results demonstrate that employing the particle swarm optimization algorithm to guide UAVs in locating pollution sources enables them to accurately identify the sources within a short period, exhibiting a high level of effectiveness and robustness.
In order to solve the flexible flow shop scheduling problem with variable processing times (FFSP- VPT), this paper analyzes the choice of processing stages with variable processing times, the quality inspection of unqualified products, and the occurrence of rework. As such, the FFSP- VPT mathematical model is established, adopting the fine scheduling process, which changes the traditional rough scheduling to guide the workshop production process. In addition, it not only provides the workshop with some adjustable stages, namely, the stages with variable processing times, but also searches for the balance between processing time and rework rate so as to ensure the processing quality while minimizing the maximum completion time. Based on the traditional Shuffled Frog-Leaping Algorithm(SFLA), we introduce the self-adaptive factor into the maximum update distance of SFLA algorithm, proposing a self-adaptive shuffled frog-leaping algorithm as the global optimization algorithm. What's more, through simulation test, the test result of the proposed algorithm is compared with those of other algorithms, which verifies the effectiveness of the SFLA algorithm in solving practical FFSP- VPT problems.
The detection of gas molecules is critical for environmental monitoring, chemical process control, agriculture, and medical applications. Therefore, gas sensors and electronic noses (e-nose) are widely studied by researchers all over the world. Graphene has been considered to be a promising gas detection material due to its special electronic properties, which are strongly influenced by the adsorption of extrinsic molecules. Doping of metal oxides and nanometal particles has also been extensively studied and their electrical property is highly sensitive to the properties of absorbed gases. Carbon nanotubes (CNTs) are expensive but have advantages of high sensitivity, good reversibility, and nice stability. Several research groups have studied the mixed structure of the above three materials with their derivatives blended, which show improved gas sensing capabilities. This review summarizes the state-of-the-art progresses in the research on gas sensors and e-nose, based on graphene, metal oxide, and CNTs.
A double D-shaped optical fiber for simultaneous temperature and relative humidity (RH) sensing is described. The flat surfaces of the D-shaped optical fiber are coated with toluene as a thermally sensitive material and polyethylene as a humidity-sensitive material. Using the finite element method, the effects of the metal film thickness and polished depth of the D-shaped optical fiber on the sensing performance were analyzed and the optimal structural parameters were obtained. The temperature and humidity sensing properties were studied and show that temperature and humidity sensitivities of 1.02 nm/°C and 0.79 nm/% RH were obtained from 25 °C to 65 °C and 30% RH and 70% RH with maximum linearities of 0.996 and 0.997. The proposed optical fiber sensor demonstrates potential applications for dual-parameter detection.
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