Learning for Control: An Inverse Optimization Approach

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Abstract

We present a learning method to learn the mapping from an input space to an action space, which is particularly suitable when the action is an optimal decision with respect to a certain unknown cost function. We use an inverse optimization approach to retrieve the cost function by introducing a new loss function and a new hypothesis class of mappings. A tractable convex reformulation of the learning problem is also presented. The method is effective for learning input-action mapping in continuous input-action space with input-output constraints, typically present in control systems. The learning approach can be effectively transformed to learn a Model Predictive Control (MPC) behaviour and a case study to mimic an MPC is presented, which is a rather computationally heavy control strategy. Simulation and experimental results show the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)187-192
JournalIEEE Control Systems Letters
Volume6
DOIs
Publication statusPublished - 2022

Keywords

  • Aerospace electronics
  • convex reformulation.
  • Cost function
  • inverse optimization
  • Learning-based control
  • Least mean squares methods
  • Linear programming
  • supervised learning
  • Supervised learning
  • Symmetric matrices
  • Training

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