Abstract
We propose a model-predictive control (MPC)-based approach to solve a
human-in-the-loop control problem for a network system lacking sensors
and actuators to allow for a fully automatic operation. The humans in
the loop are, therefore, essential; they travel between the network
nodes to provide the remote controller with measurements and to actuate
the system according to the controller’s commands. Time instant
optimization MPC is utilized to compute when the measurement and
actuation actions are to take place to coordinate them with the network
dynamics. The time instants also minimize the burden of human operators
by tracking their energy levels and scheduling the necessary breaks.
Fuel consumption related to the operators’ travel is also minimized. The
results in a digital twin of the Dez Main Canal illustrate that the new
algorithm outperforms previous methods in terms of meeting operational
objectives and taking care of human well-being, but at the cost of
higher computational requirements.
Original language | English |
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Pages (from-to) | 4610-4622 |
Number of pages | 13 |
Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
Volume | 53 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Human-in-the-loop
- Irrigation
- model-predictive control (MPC)
- Network systems
- network systems
- Predictive control
- Schedules
- Sensors
- Stress
- Time measurement