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Designing a Controller with Image-based Pipelined Sensing and Additive Uncertainties

Published: 12 September 2019 Publication History

Abstract

Pipelined image-based control uses parallel instances of its image-processing algorithm in a pipelined fashion to improve the quality of control. A performance-oriented control design improves the controller settling time with each additional processing resource, which creates a resources-performance trade-off. In real-life applications, it is common to have a continuous-time model with additive uncertainties in one or more parameters that may affect the controller performance and the aforementioned trade-off. We present a robustness analysis framework for performance-oriented pipelined controllers with additive model uncertainties. We present a technique to obtain discrete-time uncertainties based on the continuous-time uncertainties for given uncertainty bounds. To benchmark such uncertainty bounds for a real system, we consider uncertainties in one element of the system, potentially caused by multiple uncertain parameters in the model. Robustness and its impact in the trade-off analysis are studied. We also provide a robustness-oriented pipelined controller design that takes into account the benchmarked uncertainties. Our results show that in performance-oriented designs, the tolerable uncertainties for a pipelined controller decrease when increasing the number of pipes. In robustness-oriented designs, the controller robustness is enhanced with each newly added pipe. We show the feasibility of our technique by implementing a realistic example in a Hardware-in-the-Loop simulation.

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Cited By

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  • (2021)Reconfigurable Pipelined Control SystemsIEEE Design & Test10.1109/MDAT.2020.300680338:5(17-24)Online publication date: Oct-2021
  • (2021)Optimizing Multiprocessor Image-Based Control Through Pipelining and ParallelismIEEE Access10.1109/ACCESS.2021.31030519(112332-112358)Online publication date: 2021
  • (2020)Adaptive predictive control for pipelined multiprocessor image-based control systems considering workload variations2020 59th IEEE Conference on Decision and Control (CDC)10.1109/CDC42340.2020.9303827(5236-5242)Online publication date: 14-Dec-2020

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Published In

cover image ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems  Volume 3, Issue 3
Special Issue on Real Time Aspects in CPS and Regular Papers (Diamonds)
July 2019
269 pages
ISSN:2378-962X
EISSN:2378-9638
DOI:10.1145/3356396
  • Editor:
  • Tei-Wei Kuo
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 September 2019
Accepted: 01 April 2019
Revised: 01 January 2019
Received: 01 January 2018
Published in TCPS Volume 3, Issue 3

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Author Tags

  1. Image-based control
  2. LQR tuning
  3. particle swarm optimization
  4. pipelined sensing control
  5. robustness analysis
  6. trade-off analysis

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Cited By

View all
  • (2021)Reconfigurable Pipelined Control SystemsIEEE Design & Test10.1109/MDAT.2020.300680338:5(17-24)Online publication date: Oct-2021
  • (2021)Optimizing Multiprocessor Image-Based Control Through Pipelining and ParallelismIEEE Access10.1109/ACCESS.2021.31030519(112332-112358)Online publication date: 2021
  • (2020)Adaptive predictive control for pipelined multiprocessor image-based control systems considering workload variations2020 59th IEEE Conference on Decision and Control (CDC)10.1109/CDC42340.2020.9303827(5236-5242)Online publication date: 14-Dec-2020

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