Simultaneous optimal parameter and mode transition time estimation

Lauren M. Miller; Todd David Murphey

doi:10.1109/IROS.2012.6385707

Simultaneous optimal parameter and mode transition time estimation

Lauren M. Miller^*, Todd David Murphey

^*Corresponding author for this work

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

This paper presents a method of simultaneous mode transition time and parameter estimation for hybrid systems based on switching time optimization techniques. A concise derivation of first- and second-order optimality conditions with respect to both mode transition times and parameter values is presented, including cross-derivative terms between the switching times and the parameters. The estimation algorithm is shown to be effective for estimating transition times as well as unknown parameter values from coarsely sampled data for a skid-steered vehicle, which traverses unknown or changing terrain and transitions between discrete dynamic modes. It is shown that second-order optimization methods using the exact Hessian provide far superior convergence, compared to first-or approximate second-order methods, to correct values in simulated and experimental scenarios.

Original language	English (US)
Title of host publication	2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
Pages	719-724
Number of pages	6
DOIs	https://doi.org/10.1109/IROS.2012.6385707
State	Published - Dec 1 2012
Event	25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve, Portugal Duration: Oct 7 2012 → Oct 12 2012

Other

Other	25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
Country	Portugal
City	Vilamoura, Algarve
Period	10/7/12 → 10/12/12

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Vision and Pattern Recognition
Computer Science Applications

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Cite this

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title = "Simultaneous optimal parameter and mode transition time estimation",

abstract = "This paper presents a method of simultaneous mode transition time and parameter estimation for hybrid systems based on switching time optimization techniques. A concise derivation of first- and second-order optimality conditions with respect to both mode transition times and parameter values is presented, including cross-derivative terms between the switching times and the parameters. The estimation algorithm is shown to be effective for estimating transition times as well as unknown parameter values from coarsely sampled data for a skid-steered vehicle, which traverses unknown or changing terrain and transitions between discrete dynamic modes. It is shown that second-order optimization methods using the exact Hessian provide far superior convergence, compared to first-or approximate second-order methods, to correct values in simulated and experimental scenarios.",

author = "Miller, {Lauren M.} and Murphey, {Todd David}",

year = "2012",

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doi = "10.1109/IROS.2012.6385707",

language = "English (US)",

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note = "25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 ; Conference date: 07-10-2012 Through 12-10-2012",

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Miller, LM & Murphey, TD 2012, Simultaneous optimal parameter and mode transition time estimation. in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012., 6385707, pp. 719-724, 25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012, Vilamoura, Algarve, Portugal, 10/7/12. https://doi.org/10.1109/IROS.2012.6385707

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AB - This paper presents a method of simultaneous mode transition time and parameter estimation for hybrid systems based on switching time optimization techniques. A concise derivation of first- and second-order optimality conditions with respect to both mode transition times and parameter values is presented, including cross-derivative terms between the switching times and the parameters. The estimation algorithm is shown to be effective for estimating transition times as well as unknown parameter values from coarsely sampled data for a skid-steered vehicle, which traverses unknown or changing terrain and transitions between discrete dynamic modes. It is shown that second-order optimization methods using the exact Hessian provide far superior convergence, compared to first-or approximate second-order methods, to correct values in simulated and experimental scenarios.

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