Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System

Magnetic Levitation is a process in which an object is suspended with the support of the magnetic field. Despite being an unstable system, Magnetic Levitation Systems (MAGLEV) have profound applications in various fields of engineering. MAGLEV systems are sensitive, unstable, and nonlinear and uncertainties always pose a challenge in Controller Design. As a solution, adaptive controllers came into existence with adaptation mechanisms to cover the system uncertainties. In this study, a simple, novel, and an effective approach to the Enhanced Adaptive Control scheme is proposed for the ball position control and tracking of an unstable Magnetic Levitation System. The proposed Enhanced Model Reference Adaptive Scheme (EMRAC) follows the same phenomenon of the Model Reference Adaptive Scheme (MRAC) with a slight difference in its control strategy. The proposed scheme consists of Proportional-Integral-Velocity plus Feed Forward as the control structure and a modified version of the standard tuning rule is used as the adaptation mechanism. The control scheme is applied to a standard benchmark Magnetic Levitation System and the tracking performance of the scheme is tested by applying square and multi-sine pattern trajectories to the Magnetic Levitation System. The performance of the developed Enhanced MRAC performance is compared with that of the Proportional Integral Velocity with Feedforward Control (PIV+FF) scheme and the proposed control scheme is proven to be more suitable. The performance of the proposed scheme is also analyzed with Power Spectral Density and Root Mean Square Error to evaluate the ball position tracking control. It is inferred from the experimental results that Enhanced MRAC accommodates the changes and makes the system more reliable with good tracking ability

A wireless and battery-free wound infection sensor based on DNA hydrogel

The confluence of wireless technology and biosensors offers the possibility to detect and manage medical conditions outside of clinical settings. Wound infections represent a major clinical challenge in which timely detection is critical for effective interventions, but this is currently hindered by the lack of a monitoring technology that can interface with wounds, detect pathogenic bacteria, and wirelessly transmit data. Here, we report a flexible, wireless, and battery-free sensor that provides smartphone-based detection of wound infection using a bacteria-responsive DNA hydrogel. The engineered DNA hydrogels respond selectively to deoxyribonucleases associated with pathogenic bacteria through tunable dielectric changes, which can be wirelessly detected using near-field communication. In a mouse acute wound model, we demonstrate that the wireless sensor can detect physiologically relevant amounts of Staphylococcus aureus even before visible manifestation of infection. These results demonstrate strategies for continuous infection monitoring, which may facilitate improved management of surgical or chronic wounds.Agency for Science, Technology and Research (A*STAR)Ministry of Health (MOH)National Medical Research Council (NMRC)Published versionJ.S.H. acknowledges support from grants from the National Research Foundation Singapore (NRFF2017-07 and AISG-GC-2019-002), Ministry of Education Singapore (MOE2016-T3-1-004), and Institute for Health Innovation and Technology. D.L.B. acknowledges support from the Agency for Science, Technology and Research (A*STAR) under its Industry Alignment Fund–Pre-Positioning Programme (IAF-PP) grant (H17/01/a0/0C9) as part of the Wound Care Innovation for the Tropics Programme, IAF-PP grant (H17/01/a0/004), and Skin Research Institute of Singapore, Phase 2: SRIS@Novena. H.L. acknowledges support from the Wound Care Innovation for the Tropics Programme, A*STAR IAF-PP grant (H19/01/a0/0GG9), Skin Innovation grant (SIG18005), MOE AcRF Tier 1 grant (R-143-000-B79-114), and Singapore Ministry of Health’s National Medical Research Council OF-IRG (MOH-000612-00). W.L. acknowledges support from MOE AcRF Tier 1 grant (R-221-000-093-133). B.C.K.T. acknowledges support from National University of Singapore Startup Grant (NUS-2017-01) and Agency of Science Technology and Research Singapore (A18A1B0045). H. Yao acknowledges Research Scholarship from NUS Materials Science and Engineering. Y.G. acknowledges support from the EMULSION Programme H18/01/A0/017 (IAF-PP, A*STAR). S.M.P.K. acknowledges support from the National Research Foundation Singapore, under its NRF Large Equipment Grants–Grant Addendum 3: Operations of the Singapore Synchrotron Light Source (SSLS)