Control is intrinsic to biological organisms, whose cells are in a constant state of sensing and response to numerous external and self-generated stimuli. Diverse means are used to study the complexity through control-based approaches in these cellular systems, including through chemical and genetic manipulations, input-output methodologies, feedback approaches, and feed-forward approaches. We first discuss what happens in control-based approaches when we are not actively examining or manipulating cells. We then present potential methods to determine what the cell is doing during these times and to reverse-engineer the cellular system. Finally, we discuss how we can control the cell's extracellular and intracellular environments, both to probe the response of the cells using defined experimental engineering-based technologies and to anticipate what might be achieved by applying control-based approaches to affect cellular processes. Much work remains to apply simplified control models and develop new technologies to aid researchers in studying and utilizing cellular and molecular processes.
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http://dx.doi.org/10.1146/annurev-bioeng-071910-124651 | DOI Listing |
PLoS One
January 2025
Department of Orthopedics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Ratchathewi, Bangkok, Thailand.
Among control methods for robotic exoskeletons, biologically inspired control based on central pattern generators (CPGs) offer a promising approach to generate natural and robust walking patterns. Compared to other approaches, like model-based and machine learning-based control, the biologically inspired control provides robustness to perturbations, requires less computational power, and does not need system models or large learning datasets. While it has shown effectiveness, a comprehensive evaluation of its user experience is lacking.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
View Article and Find Full Text PDFNPP Digit Psychiatry Neurosci
January 2025
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA.
J Neuroeng Rehabil
December 2024
Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
Background: This research aims to improve the control of assistive devices for individuals with hemiparesis after stroke by providing intuitive and proportional motor control. Stroke is the leading cause of disability in the United States, with 80% of stroke-related disability coming in the form of hemiparesis, presented as weakness or paresis on half of the body. Current assistive exoskeletonscontrolled via electromyography do not allow for fine force regulation.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer and Information Science (CIS), Faculty of Technoscience, Muni University, Arua, Uganda.
The triple-active bridge (TAB) converter is widely used in various applications due to its high efficiency and power density. However, the high-frequency (HF) transformer coupling between the ports presents challenges for controller design. This article presents a model predictive control (MPC) approach based on single-phase shift modulation for the TAB converter.
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