Extensions to experiment design and identification algorithms for large-scale and stochastic processes
by Kaushik Subramanian, Siddhartha Kumar, Sachin C. Patwardhan, Vinay Prasad
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 3, No. 1, 2011

Abstract: We explore extensions of optimal experiment design algorithms for large-scale deterministic catalytic kinetic systems, and of system identification and control techniques for stochastic thin film deposition systems. In the case of optimal experiment design, we suggest the use of principal component analysis and clustering to identify similarities in parameters and their sensitivities. This enables the grouping of similar parameters together, thus reducing the dimensionality before applying a D-optimal experimental design, and ensuring identifiability of the selected parameters and better conditioning of the Fischer information matrix. In the case of thin film deposition, since the stochastic deposition process is simulated using kinetic Monte Carlo models, closed form models that can be used for control are not available. We present modifications to proper orthogonal decomposition methods for non-ergodic systems, and methods for reparameterisation of autoregressive integrated moving average models for identification of compact closed form models and model predictive control.

Online publication date: Wed, 18-Mar-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Mechatronic Systems (IJAMECHS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com