Non-dimensional mathematical model of brushless DC motor (BLDCM) system is presented here. BLDCM is known to produce chaotic phenomenon under certain conditions. This paper fuses dynamic surface control, radial basis function neural network, and adaptive technology to control the BLDCM, which overcomes the repetitive differentiation of the nonlinear terms of backstepping and the boundedness hypothesis of control gain predetermined. The tangent barrier Lyapunov function is also used for time-delay nonlinear system with parametric uncertainties. Simulation results under different conditions indicate that the proposed method works well to suppress chaos and effects of parameter variation.
This paper focuses on the problem of an adaptive neural network dynamic surface control (DSC) based on disturbance observer for the wheeled mobile robot with uncertain parameters and unknown disturbances. The nonlinear observer is used to compensate for the external disturbance, and the neural network is employed to approximate the uncertain and nonlinear items of system. Then, the Lyapunov theory is introduced to demonstrate the stabilization of the proposed control algorithm. Finally, the simulation results illustrate that the proposed algorithm not only is superior to conventional DSC in trajectory tracking and external friction disturbance compensation but also has better response, adaptive ability, and robustness.
This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
Background. Biliary atresia (BA) is an uncommon illness that causes the bile ducts outside and within the liver to become clogged in babies. If left untreated, the cholestasis causes increasing conjugated hyperbilirubinemia, cirrhosis, and hepatic failure. BA has a complicated aetiology, and the mechanisms that drive its development are unknown. The objective of this study was to show the role of probable critical genes involved in the pathophysiology of biliary atresia. Methods. We utilised the public Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE46960 to find differentially expressed genes (DEGs) in 64 biliary atresia newborns, 14 infants with various causes of intrahepatic cholestasis, and 7 deceased-donor children as control subjects in our study. The relevant information was looked into. The important modules were identified after functional enrichment, GO and KEGG pathway analyses, protein-protein interaction (PPI) network analyses, and GSEA analysis. Results. The differential expression analysis revealed a total of 22 elevated genes. To further understand the biological activities of the DEGs, we run functional enrichment analyses on them. Meanwhile, KEGG analysis has revealed significant enrichment of pathways involved in activating cross-talking with inflammation and fibrosis in BA. SERPINE1, THBS1, CCL2, MMP7, CXCL8, EPCAM, VCAN, ITGA2, AREG, and HAS2, which may play a significant regulatory role in the pathogenesis of BA, were identified by PPI studies. Conclusion. Our findings suggested 10 hub genes and probable mechanisms of BA in the current study through bioinformatic analysis.
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