Skip to main content
Article thumbnail
Location of Repository

Self-tuning control of non-linear systems using gaussian process prior models

By D. Sbarbaro and R. Murray-Smith

Abstract

Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution) in areas of high uncertainty. As a consequence, the controller has dual features, since it both tracks a reference signal and learns a model of the system from observed responses. The general method and its unique features are illustrated on simulation examples

Topics: QA75
Publisher: Springer
Year: 2005
OAI identifier: oai:eprints.gla.ac.uk:3720
Provided by: Enlighten

Suggested articles

Citations

  1. (2001). A.: Gaussian process priors with ARMA noise models. In:
  2. (1995). Adaptive control of a class of nonlinear discrete-time systems. doi
  3. (1995). Adaptive dual control methods: An overview. In: doi
  4. (1998). Adaptive dual controller for a class of nonlinear systems. In: doi
  5. (1998). Bayesian Gaussian processes for regression and classification.
  6. (2003). D.M.: Bayesian regression and classification using mixtures of multiple Gaussian processes. doi
  7. (2003). Derivative observations in Gaussian process models of dynamic systems.
  8. (1998). Dual adaptive control of stochastic systems using neural networks. doi
  9. (1997). Dual pole placement controller with direct adaptation. doi
  10. (1996). Evaluation of Gaussian Process and other Methods for non-linear regression.
  11. (2003). Fast forward selection to speed up sparse Gaussian process regression. In:
  12. (2003). Gaussian process priors with uncertain inputs – application to multiple-step ahead time series forecasting.
  13. (1997). Gaussian processes: A replacement for supervised neural networks? In: Lectures notes for the NIPS 1997, Denver,
  14. (1990). Identification and control of dynamical systems using neural networks. doi
  15. (1997). Monte Carlo implementation of Gaussian process models for Bayesian regression and classification.
  16. (1997). Neural implementation of GMV control shemes based on affine input/output models. doi
  17. (2002). Nonlinear adaptive control using non-paramtric gaussian process prior models. In: doi
  18. (2000). Nonlinear structure identification: A gaussian process prior/velocity-based approach. In:
  19. (1978). On curve fitting and optimal design for regression (with discussion).
  20. (1999). On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures. In:
  21. (1998). Prediction with Gaussian process: From linear regression to linear prediction and beyond. doi
  22. (1987). Self-tuning controllers for nonlinear systems. Automatica doi
  23. (1994). Self-tuning neurocontrol of nonlinear systems using localized polynomial networks with CLI cells. In: doi
  24. (2001). Some Bayesian numerical analysis.
  25. (2001). Sparse Bayesian learning and the relevance vector machine. doi
  26. (2000). Survey of adaptive dual control methods. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.