Model Development, Uncertainty Quantification and Control Design for Nonlinear Smart Material Systems
Document Type
Presentation Abstract
Presentation Date
10-1-2012
Abstract
Piezoelectric, magnetic and shape memory alloy (SMA) materials offer unique capabilities for energy harvesting and reduced energy requirements in aerospace, aeronautic, automotive, industrial and biomedical applications. For example, the capability of lead zirconate titanate (PZT) to convert electrical to mechanical energy, and conversely, poises it for energy harvesting whereas the large work densities of SMA are being utilized in applications including efficient jet engine design. However, all of these materials exhibit creep, rate-dependent hysteresis, and constitutive nonlinearities that must be incorporated in models and designs to achieve their full potential. Furthermore, models and control designs must be constructed in a manner that incorporates parameter and model uncertainties and permits predictions with quantified uncertainties. In this presentation, we will discuss Bayesian techniques to quantify uncertainty in nonlinear distributed models arising in the context of smart systems. We will also discuss the role of these techniques for subsequent robust control design.
Recommended Citation
Smith, Ralph C., "Model Development, Uncertainty Quantification and Control Design for Nonlinear Smart Material Systems" (2012). Colloquia of the Department of Mathematical Sciences. 408.
https://scholarworks.umt.edu/mathcolloquia/408
Additional Details
Monday, 1 October 2012
3:10 p.m. in Math 103
4:00 p.m. Refreshments in Math Lounge 109