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Measurement and control of emergent phenomena emulated by resistive-capacitive networks, using fractionalorder internal model control and external adaptive control

Galvão, R. K. H. and Hadjiloucas, S. (2019) Measurement and control of emergent phenomena emulated by resistive-capacitive networks, using fractionalorder internal model control and external adaptive control. Review of Scientific Instruments, 90. 103003. ISSN 0034-6748

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To link to this item DOI: 10.1063/1.5097743

Abstract/Summary

A fractional-order internal model control technique is applied to a three-dimensional resistive-capacitive network to enforce desired closed loop dynamics of first order. In order to handle model mismatch issues resulting from the random allocation of the components within the network, the control law is augmented with a model-reference adaptive strategy in an external loop. By imposing a control law on the system to obey first order dynamics, a calibrated transient response is ensured. The methodology enables feedback control of complex systems with emergent responses and is robust in the presence of measurement noise or under conditions of poor model identification. Furthermore, it is also applicable to systems that exhibit higher order fractional dynamics. Examples of feedback-controlled transduction include cantilever positioning in atomic force microscopy or the control of complex de-excitation lifetimes encountered in many types of spectroscopies, e.g., nuclear magnetic, electron-spin, microwave, multiphoton fluorescence, Förster resonance, etc. The proposed solution should also find important applications in more complex electronic, microwave, and photonic lock-in problems. Finally, there are further applications across the broader measurement science and instrumentation community when designing complex feedback systems at the system level, e.g., ensuring the adaptive control of distributed physiological processes through the use of biomedical implants.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Chemical Analysis Facility (CAF)
Interdisciplinary centres and themes > Centre for Integrative Neuroscience and Neurodynamics (CINN)
Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:86868
Publisher:AIP

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