Why the study?
A control approach was needed for network systems lacking sensors and actuators for fully automatic operation, requiring humans in the loop to travel between nodes for measurement and actuation.
Absolute Event Rate: 261.65% vs 339.75%
A novel time instant optimization model-predictive control algorithm improves operational objectives and operator well-being in human-in-the-loop network systems like irrigation canals, though with higher computational costs.
Simulation results suggest gains in canal control; leaves open real-world validation before operational use.
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.
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Sadowska et al. (2023) studied Irrigation canal management. Time instant optimization mobile model predictive control (TIO-MoMPC) vs. Original MoMPC was evaluated on Posterior performance index (Joper. obj.).
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