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AMD - Toolbox for computing separating input in set-membership identification of affine systems

This is the code of active model discrimination (AMD) that computes the separating input for set-membership identification of affine systems. Please refer to the reference:

Ding, Y., Harirchi F., Yong, S. Z., Jacobsen, E., and Ozay, N. (2018). Optimal input design for affine model discrimination with applications in intention-aware vehicles, in Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems, PP. 297-307, arXiv:1702.01112.

List of capabilities:

  • Driver intention estimation for two driving scenarios: lane changing and intersection crossing
  • Concatenate the affine dynamics over a finite horizon and a finite number of model/member pairs
  • Convert semi-infinite constraint to linear constraint by leveraging robust optimization
  • Implement double negation to find equivalence of the (non-convex) separability condition
  • Solve a mixed integer linear program (MILP) to get the separating input for set-membership identification
    • Polyhedral constraints on input, controlled state, noise
  • Compare with previous work
    • Harirchi F., Yong, S. Z., Jacobsen E., and Ozay, N. (2017). Active mode discrimination with applications to fraud
      detection in smart buildings. IFAC-PapersOnLine, 50(1):9527-9534.

Requirements

Notes

  • Gurobi 8.1.1 may result in infeasible sultion for intersection crossing scenario.

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