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A Plug-and-Play Realization of Decentralized Feedback Control for Smart Lighting Systems
Abstract
This paper proposes decentralized feedback controllers with plug-and-play capability for a class of smart lighting systems. In these systems, each light fixture has a spectrally tunable light source and a multichannel color sensor, and no communication is required between fixtures. Further, the light transport in the illuminated space is diagonally dominant, i.e., the light sensed by each sensor is primarily from the source in the same fixture. The controller design problems are formulated and solved for two cases of decentralized setpoint tracking and decentralized quadratic optimal control (with tracking error and energy penalty). For the decentralized setpoint tracking problem, a mechanism is proposed to automatically determine the feedback gains using individual sensor measurements in a plug-and-play fashion without the knowledge of the light transport model of the illuminated space. For the decentralized quadratic optimal control, the design problem is approximated with a series of local optimization problems, which are solved in a decentralized manner using individual sensor measurements. A suboptimality bound for the solution of the approximated problem compared with the global optimum is obtained. The performance of the suggested controllers in terms of achievement of a desired setpoint and daylight harvesting for energy saving is validated and evaluated based on typical lighting parameters in a room-scale experimental testbed with light-emitting diode fixtures and color sensors.
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Color sensors can also be part of smart lighting.
“Afshari and Mishra [99] reveal that color sensors can also be part of smart lighting.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Afshari and Mishra [99]
Color sensors are used for decentralized control and optimizing setpoint targets.
“Color sensors are used for decentralized control and optimizing setpoint targets.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Afshari and Mishra [99]
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Sorted by citations
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“[99] reveal that color sensors can also be part of smart lighting.”
Background
Metadata
- Authors
- Afshari, S., & Mishra, S.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- ORCID
- Mishra, Sandipan
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2016-07
- Volume
- 24
- Issue
- 4
- Pages
- 1317-1327
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2344796802
- Cited by
- 34
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 6 · 2021: 6 · 2020: 2 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 1
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Control Systems Technology (publishedVersion)
- Funders
- Engineering Research Centers Program through the National Science Foundation (NSF) within the NSF Cooperative Agreement (EEC-0812056)
- New York State Foundation for Science, Technology and Innovation (NYSTAR C130145)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Keywords
- decentralized feedback control, plug-and-play control, smart lighting, color sensor, light transport, diagonal dominance, quadratic optimal control, suboptimal control, daylight harvesting, energy conservation, local optimization
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