Predictive Control of a Human–in–the–Loop Network System Considering OperatorComfort Requirements
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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.