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Model Calibration

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Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 93)

Abstract

Any model, regardless of its nature, is characterized by parameters, i.e., unknown quantities that need to be determined in order to solve the model. Often, parameters are directly measurable on the target system (e.g., the weight, the size, or the speed of a robot), they can be obtained from data sheets or computed from physical laws (e.g., friction and restitution coefficients, inertia matrices, etc.). However, more abstract models might involve so-called free parameters that result from the lumping of several phenomena, some of which might be difficult to model explicitly, thereby rendering direct approaches inapplicable. In such cases, more sophisticated numerical methods and machine learning techniques are necessary.

Keywords

  • Model Calibration
  • Step Length
  • Simulated Trajectory
  • Orthogonal Experiment
  • Geometrical Approximation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Grégory Mermoud .

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Mermoud, G. (2014). Model Calibration. In: Stochastic Reactive Distributed Robotic Systems. Springer Tracts in Advanced Robotics, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-02609-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-02609-1_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02608-4

  • Online ISBN: 978-3-319-02609-1

  • eBook Packages: EngineeringEngineering (R0)