Predictive Control of a Human–in–the–Loop Network System Considering Operator Comfort Requirements

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.