Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems

Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based on backstepping or dynamic surface control (DSC) methods, requiring full system states to be measurable. The neural networks (NNs) or fuzzy logic systems (FLSs) used to accommodate uncertainties also impose demanding computational cost and sluggish convergence. To address these issues, this paper proposes a new output-feedback control for uncertain pure-feedback systems without using backstepping and function approximator. A coordinate transform is first used to represent the pure-feedback system in a canonical form to evade using the backstepping or DSC scheme. Then the Levant's differentiator is used to reconstruct the unknown states of the derived canonical system. Finally, a new unknown system dynamics estimator with only one tuning parameter is developed to compensate for the lumped unknown dynamics in the feedback control. This leads to an alternative, simple approximation-free control method for pure-feedback systems, where only the system output needs to be measured. The stability of the closed-loop control system, including the unknown dynamics estimator and the feedback control is proved. Comparative simulations and experiments based on a PMSM test-rig are carried out to test and validate the effectiveness of the proposed method

Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction, and medical education, and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized the biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this first-of-its-kind survey can provide a comprehensive overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health

Effect of Ultrasonic Surface Rolling Process on Surface Properties and Microstructure of 6061 Aluminum Alloy

Nano-surface layers were prepared on the surface of 6061 aluminum alloy using the ultrasonic surface rolling process (USRP). The surface morphology, surface roughness, microstructure, hardness, and corrosion resistance of 6061 aluminum alloy were systematically characterized using X-ray diffraction (XRD), laser scanning confocal microscopy (LSCM), optical microscope(OM), scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), and other testing methods. The results showed that ultrasonic surface rolling strengthening did not change the surface phase composition of 6061 aluminum alloy. It changed the size of the surface phases and the distance between the phases while refining the surface grains. The static pressures has a great influence on the surface properties of 6061 aluminum alloy. The best surface properties were obtained under 500N static pressures. The surface hardness reached 129.5HV0.5, the surface morphology was flat and continuous, the surface roughness was reduced to Ra0.191μm, and the corrosion resistance was significantly improved

Recrystallization-based grain boundary engineering of additively manufactured metals

Grain boundary engineering (GBE) is a materials processing strategy to enhance the physical and mechanical properties of polycrystalline metals by purposely incorporating special types of grain boundaries—such as twin boundaries (TBs)—in the microstructure. Conventional GBE methods involve multiple strain-annealing cycles, which change the geometry of the target material substantially. Thus, they are unfit to parts produced using near-net-shape manufacturing techniques, including metal additive manufacturing (AM). Devising a GBE method that is compatible with AM, however, would allow to further enhance the performance of topology-optimized structural components by improving their GB-controlled properties. This thesis focuses on devising an AM-compatible processing strategy to achieve this goal, which I refer to as additive-GBE (AGBE). Focusing on stainless steel 316L as a case-study material, I test and demonstrate different AGBE methodologies using different AM processes, including directed energy deposition (DED) and laser powder bed fusion (LPBF). All these methodologies rely on the ability to trigger microstructure recrystallization “on demand”. Indeed, recrystallization of austenitic steel produces microstructures containing a multitude of TBs. To drive recrystallization in DED, I apply single point incremental forming to introduce controlled mechanical strains into the build in-situ. This approach yields gradient or “sandwiched” microstructures characterized by variable TBs distributions. In LPBF, I gain control over the thermal stability of the alloy—and thus its propensity to undergo recrystallization—by tuning the residual strains in the microstructure (which mostly consist of geometrically necessary dislocations) as well as the chemical heterogeneity of the solidification structure. I demonstrate how both factors can be manipulated independently by changing the LPBF process parameters to vary the driving force for nucleation of recrystallized grains as well as their growth rate in the microstructure, respectively. Using different combinations of such process parameters, I show the possibility of producing samples of stainless steel 316L that combine arbitrary distributions of recrystallized and as-built microstructures. Finally, I analyze the mechanical behavior of the AGBE alloys produced by DED and LPBF and investigate the additional strengthening mechanisms brought about by the engineered microstructure heterogeneity. The results suggest the possibility of using AGBE as a cost-effective and practical approach for the direct production of topology-optimized parts with controlled microstructures and improved performance. As such, AGBE broadens the microstructure-based design space of engineering materials.Doctor of Philosoph

New technologies for lead-free flip chip assembly

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