TR2021-058

Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter

    •  Berntorp, K., Chakrabarty, A., Di Cairano, S., "Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter", American Control Conference (ACC), DOI: 10.23919/​ACC50511.2021.9483251, May 2021, pp. 160-165.
      BibTeX TR2021-058 PDF
      • @inproceedings{Berntorp2021may,
      • author = {Berntorp, Karl and Chakrabarty, Ankush and Di Cairano, Stefano},
      • title = {Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter},
      • booktitle = {American Control Conference (ACC)},
      • year = 2021,
      • pages = {160--165},
      • month = may,
      • doi = {10.23919/ACC50511.2021.9483251},
      • url = {https://www.merl.com/publications/TR2021-058}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Dynamical Systems, Signal Processing

Abstract:

This paper addresses the center-of-gravity height and dynamics estimation problem, which is key for rollover prevention systems in automotive. We model the vehicle as a spring-damper system and develop a Bayesian method that outputs estimates of the center-of-gravity height, suspension stiffness and damping coefficient. We leverage the model structure to design a computationally efficient particle filter, which, combined with Bayesian optimization for particle initialization and a particle-size adaptation scheme, leads to an implementation that provides accurate, smooth estimates of CoG height, stiffness, and damping. A Monte-Carlo simulation study on a standardized maneuver shows that the method almost instantaneously provides reliable estimates that represent well the true parameter values.