1 / 11

Modelling and Control of Nonlinear Dynamic Systems with Gaussian Process Models

Modelling and Control of Nonlinear Dynamic Systems with Gaussian Process Models. Juš Kocijan 1,2. 1 Jožef Stefan Institute, Ljubljana 2 Nova Gorica Polytechnic, Nova Gorica. Introduction. The method takes roots from statistics (Bayesian approach)

taylor
Download Presentation

Modelling and Control of Nonlinear Dynamic Systems with Gaussian Process Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Modelling and Control of Nonlinear DynamicSystems with Gaussian Process Models Juš Kocijan1,2 1 Jožef Stefan Institute, Ljubljana 2 Nova Gorica Polytechnic, Nova Gorica

  2. Introduction • The method takes roots from statistics (Bayesian approach) • A Gaussian process (GP) is a collection of random variables which have a joint multivariate Gaussian distribution • Use of stochastic variables (vectors) • Relatively sophisticated theoretical background - relatively simple use • Increasingly used for applications of Neural Networks • Main questions: Modelling for control? When & why? • Probabilistic nonparametric approach to modelling of dynamic systems CAPE Forum 2004, Veszprem, February 2004

  3. GP Principle The left plot shows Gaussian prediction at new point x1, conditioned on the training points (dots) while the right plot shows predictive mean along with its 2s error bars for two points, x2 that is close to training ones and x1 that is more distant CAPE Forum 2004, Veszprem, February 2004

  4. Dynamical systems identification with GP • Dynamical systems: MAC project - EU 5th framework RTN • Multi-step-ahead predictions • From statical nonlinearities to dynamic systems: the same approach as for ANN or fuzzy models • Difference: propagation of uncertainty CAPE Forum 2004, Veszprem, February 2004

  5. Ilustrative example: • 1st order nonlinear dynamic system: • 1st order model • Function f is a GP (twodimensional regression model, D = 2) • Hyperparameters: v0, v1, w1, w2 CAPE Forum 2004, Veszprem, February 2004

  6. Validation response CAPE Forum 2004, Veszprem, February 2004

  7. Uncertainty surface Uncertainty surface (left plot) (k+1)=f(u(k),y(k)) for the GP approximation and location of training data (right plot) CAPE Forum 2004, Veszprem, February 2004

  8. Process control example – CSTR model validation CAPE Forum 2004, Veszprem, February 2004

  9. Practical considerations on modelling of dynamic systems • Nonparametric approaches are traditionally more popular in control engineering practice • Variance that comes with model gives information about model validity in the region of use • Computational issues – inverse of covariance matrix • Documentation of the model (input,output,hyperparameters,[inverse covariance matrix]) CAPE Forum 2004, Veszprem, February 2004

  10. TANH process: control results – constrained case (constraint on variance only) CAPE Forum 2004, Veszprem, February 2004

  11. Practical considerations on control with Gaussian processes • Nonparametric model of nonlinear system: predictive control strategies • NMPC not so popular due to difficulties to construct a model on a reliable and consistent basis • Practical nonlinear robust control • Computational load is a constraint • Efforts are conducted to make process control application in industrial like environment CAPE Forum 2004, Veszprem, February 2004

More Related