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Safe and Near-Optimal Gate Control: A Case Study from the Danish West Coast

Martin Kristjansen, Kim Guldstrand Larsen, Marius Mikučionis, Christian Schilling

Abstract

Ringkoebing Fjord is an inland water basin on the Danish west coast separated from the North Sea by a set of gates used to control the amount of water entering and leaving the fjord. Currently, human operators decide when and how many gates to open or close for controlling the fjord's water level, with the goal to satisfy a range of conflicting safety and performance requirements such as keeping the water level in a target range, allowing maritime traffic, and enabling fish migration. Uppaal Stratego. We then use this digital twin along with forecasts of the sea level and the wind speed to learn a gate controller in an online fashion. We evaluate the learned controllers under different sea-level scenarios, representing normal tidal behavior, high waters, and low waters. Our evaluation demonstrates that, unlike a baseline controller, the learned controllers satisfy the safety requirements, while performing similarly regarding the other requirements.

Safe and Near-Optimal Gate Control: A Case Study from the Danish West Coast

Abstract

Ringkoebing Fjord is an inland water basin on the Danish west coast separated from the North Sea by a set of gates used to control the amount of water entering and leaving the fjord. Currently, human operators decide when and how many gates to open or close for controlling the fjord's water level, with the goal to satisfy a range of conflicting safety and performance requirements such as keeping the water level in a target range, allowing maritime traffic, and enabling fish migration. Uppaal Stratego. We then use this digital twin along with forecasts of the sea level and the wind speed to learn a gate controller in an online fashion. We evaluate the learned controllers under different sea-level scenarios, representing normal tidal behavior, high waters, and low waters. Our evaluation demonstrates that, unlike a baseline controller, the learned controllers satisfy the safety requirements, while performing similarly regarding the other requirements.

Paper Structure

This paper contains 24 sections, 7 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Problem Description.
  3. Requirements.
  4. Related Work
  5. Outline.
  6. Online Reinforcement Learning
  7. Model for Predicting Water Levels
  8. Modeling Using U[2]PPAALUPPAAL[2]UPPAALU[2]PPAAL S[2]TRATEGOSTRATEGO[2]STRATEGOS[2]TRATEGO
  9. Automaton model.
  10. Learning objective.
  11. Experimental Setup
  12. Uncertainty in Forecasts
  13. Scenarios With Different Sea Levels
  14. Baseline Controller
  15. Learning Parameters and Weights
  16. ...and 9 more sections

Figures (12)

  • Figure 1: Satellite image of the 14 gates and the sluice lock at Hvide Sande harbor.
  • Figure 2: A 3D model provided by the DCA of the 14 sluices.
  • Figure 3: General setup of reinforcement learning.
  • Figure 4: View of a single open gate and the values used to compute the area through which water flows between the fjord and the sea.
  • Figure 5: Automaton for the fjord's water level . It makes use of the built-in function T1, returning the absolute value of a floating-point expression.
  • ...and 7 more figures