TR2021-084

Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC

    •  Quirynen, R., Berntorp, K., "Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC", IFAC Conference on Nonlinear Model Predictive Control, DOI: 10.1016/​j.ifacol.2021.08.527, July 2021, pp. 76-82.
      BibTeX TR2021-084 PDF
      • @inproceedings{Quirynen2021jul,
      • author = {Quirynen, Rien and Berntorp, Karl},
      • title = {Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC},
      • booktitle = {IFAC Conference on Nonlinear Model Predictive Control},
      • year = 2021,
      • pages = {76--82},
      • month = jul,
      • doi = {10.1016/j.ifacol.2021.08.527},
      • url = {https://www.merl.com/publications/TR2021-084}
      • }
  • Research Areas:

    Control, Dynamical Systems, Optimization

Abstract:

Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering to perform high-accuracy propagation of mean and covariance information for the nonlinear system dynamics in a tractable approximation of the stochastic optimal control problem. In addition, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is presented to considerably reduce the computational cost and allow a real-time implementation of the resulting SNMPC. The prediction accuracy and control performance of the proposed approach are illustrated on a vehicle control application subject to external disturbances, while highlighting a worst-case computation time of 10 ms for SNMPC which is close to that of deterministic NMPC for this particular case study.

 

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