http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Fixed‑time non‑singular terminal sliding mode control for PMSM drive systems
Huixiang Liu,Keqi Mei,Lu Liu,Yafei Chang,Shihong Ding,Hanzhang Zhang,Jun Wang 전력전자학회 2024 JOURNAL OF POWER ELECTRONICS Vol.24 No.2
To further improve the response speed and anti-interference capability of permanent magnet synchronous motors (PMSMs), many advanced control algorithms have been developed. Among the various advanced controls, the fixed-time terminal sliding mode control is one of the effective methods. However, there are still some problems, e.g., too many parameters imposed on the control design in the existing results. In this paper, a fixed-time non-singular terminal sliding mode control (FTNTSMC) method is proposed for the speed loop of a PMSM drive system. First, to improve the closed-loop performance of the PMSM drive system, the relationship between the reference q-axis current and the speed of the PMSM is described in a second-order model. Next, a sliding mode surface with variable exponential coefficients is selected, and the expression of the controller is given. Then, the stability of the PMSM drive system is demonstrated by using Lyapunov functions. Using this method, the fixed-time convergence of the PMSM drive system can be realized and the occurrence of singularity phenomena can be avoided in a simpler and easier method. Finally, the effectiveness of the proposed method is verified by simulation and experimental results.
Peng Wan,Huixiang Yu,Feng Li,Pengfei Gao,Lei Zhang,Zhengzhi Zhao 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.10
The hot deformation behavior of low-density high-strength Fe–Mn–Al–C alloy steel at T = 900-1150 °C and ̇ = 0.01-10 s−1was studied by the Gleeble-3500 thermo-mechanical simulator. The rheological stress curve characteristics of the steel wereanalyzed through experimental data, and a physical constitutive model considering strain coupling was established. At thesame time, the finite element software DEFORM was used to calculate the critical damage value of the steel, and the influenceof T and ̇ on the maximum damage value was considered. By introducing the dimensionless parameter Zener–Hollomon, thecritical damage model was established. Finally, the workability of the steel was evaluated by using the intuitive processingmap technology. The results indicated that Fe–Mn–Al–C alloy steel is a positive strain rate-sensitive and a negative temperature-sensitive material, and the constitutive model considering physical parameters can well predict the rheological stress ofthe steel during hot deformation (R = 0.997). The critical damage factor of Fe–Mn–Al–C alloy steel varies with the changeof T and ̇ , and the range is 0.359-0.535. At the same time, the critical damage factor is more sensitive to ̇ . At a constantT, the damage factor decreases with the increase of ̇ . Based on the Prasad instability criterion, the dynamic material modelprocessing map and the microstructure verification after thermal compression, the rheological instability characteristics ofthe steel are mainly mechanical instability and local plastic flow, and the stable deformation area is mainly characterized bydynamic recrystallization. The optimal hot working process window of the steel is 975-1050 °C/0.01-0.032 s−1.
Service Clustering by Leveraging a Context-Sensitive Approach
Lantian Guo,Tao Yang,Huixiang Zhang,Dejun Mu,Zhe Li,Yang Li 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.12
Service technology has gained increasing popularity in recent communication software applied in many domains. With a growing number of services that share same or similar functionalities, clustering services help improve both service composition and mashup creation. To achieve service clustering, utilizing probabilistic topic model to extract and characterize the service description documents as corresponding topics is an available scheme. However, unlike short text in social networks, the descriptions of published services possess higher dimensionality and sparse functional information. With traditional LDA (Latent Dirichlet Allocation) model to implement topic extraction makes topics unclear. To address that challenge, we conduct a context sensitive approach to generate context sensitive vector for merging the words with similar context before loading to LDA model, referred to as CV-LDA (Context Vector LDA). Through F1-Measure of clustering and topic perplexity analysis in the real-world dataset, it is shown that the proposed approach outperforms traditional LDA model in service clustering.