Trajectory synthesis for fisher information maximization

Andrew D. Wilson*, Jarvis A. Schultz, Todd D. Murphey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Estimation of model parameters in a dynamic system can be significantly improved with the choice of experimental trajectory. For general nonlinear dynamic systems, finding globally "best" trajectories is typically not feasible; however, given an initial estimate of the model parameters and an initial trajectory, we present a continuous-time optimization method that produces a locally optimal trajectory for parameter estimation in the presence of measurement noise. The optimization algorithm is formulated to find system trajectories that improve a norm on the Fisher information matrix (FIM). A double-pendulum cart apparatus is used to numerically and experimentally validate this technique. In simulation, the optimized trajectory increases the minimum eigenvalue of the FIM by three orders of magnitude, compared with the initial trajectory. Experimental results show that this optimized trajectory translates to an order-of-magnitude improvement in the parameter estimate error in practice.

Original languageEnglish (US)
Article number6882246
Pages (from-to)1358-1370
Number of pages13
JournalIEEE Transactions on Robotics
Volume30
Issue number6
DOIs
StatePublished - Dec 1 2014

Keywords

  • Maximum likelihood estimation
  • Optimal control
  • Parameter estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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