TY - GEN
T1 - Challenges in set-valued model-predictive control
AU - Sprinkle, Jonathan
AU - Risso, Nathalie
AU - Altin, Berk
AU - Sanfelice, Ricardo
N1 - Funding Information:
This work is supported by the National Science Foundation under awards 1544395 and 1544396. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
Publisher Copyright:
© 2021 ACM.
PY - 2021/5/19
Y1 - 2021/5/19
N2 - In this abstract we describe a framework for computationally-aware computing through set-valued model predictive control. Model-predictive control (MPC) can enable multi-objective optimization in real-time, though it depends on accurate models through which future state values can be predicted. This abstract improves upon existing MPC approaches in that it considers the state to be a set (rather than a singleton in the state), allowing the trajectories to be given by a sequence of sets. The framework is beneficial for physical systems control where the uncertainty in future projection can be attributed to both model error, and environmental or sensor uncertainty, thus providing guarantees of performance, robustly. We provide an overview of the framework, and include discussion for its advantages.
AB - In this abstract we describe a framework for computationally-aware computing through set-valued model predictive control. Model-predictive control (MPC) can enable multi-objective optimization in real-time, though it depends on accurate models through which future state values can be predicted. This abstract improves upon existing MPC approaches in that it considers the state to be a set (rather than a singleton in the state), allowing the trajectories to be given by a sequence of sets. The framework is beneficial for physical systems control where the uncertainty in future projection can be attributed to both model error, and environmental or sensor uncertainty, thus providing guarantees of performance, robustly. We provide an overview of the framework, and include discussion for its advantages.
KW - Cyber-physical systems
KW - Model predictive control
KW - Robust control
UR - http://www.scopus.com/inward/record.url?scp=85110623443&partnerID=8YFLogxK
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U2 - 10.1145/3457335.3461708
DO - 10.1145/3457335.3461708
M3 - Conference contribution
AN - SCOPUS:85110623443
T3 - Proceedings of 2021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021
SP - 13
EP - 14
BT - Proceedings of 2021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021
PB - Association for Computing Machinery, Inc
T2 - 021 Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems, CAADCPS 2021
Y2 - 18 May 2021 through 21 May 2021
ER -