Robust Control Of Flexible Structures Using Multiple Shape Memory Alloy Actuators

The design and implementation of control strategies for large, flexible smart structures presents challenging problems. To demonstrate the capabilities of shape-memory-alloy actuators, we have designed and fabricated a three-mass test article with multiple shape-memory-alloy (NiTiNOL) actuators. The force and moment actuators were implemented on the structure to examine the effects of control structure interaction and to increase actuation force. These SMA actuators exhibit nonlinear effects due to dead band and saturation. The first step in the modeling process was the experimental determination of the transfer function matrix derived from frequency response data. A minimal state space representation was determined based on this transfer function matrix. Finally in order to reduce the order of the controller, a reduced order state space model was derived from the minimal state space representation. The simplified analytical models are compared with models developed by structural identification techniques based on vibration test data. From the reduced order model, a controller was designed to dampen vibrations in the test bed. To minimize the effects of uncertainties on the closed-loop system performance of smart structures, a LQG/LTR control methodology has been utilized. An initial standard LQG/LTR controller was designed; however, this controller could not achieve the desired performance robustness due to saturation effects. Therefore, a modified LQG/LTR design methodology was implemented to accommodate for the limited control force provided by the actuators. The closed-loop system response of the multiple input-multiple output (MIMO) test article with robustness verification has been experimentally obtained and presented in the paper. The modified LQG/LTR controller demonstrated performance and stability robustness to both sensor noise and parameter variations

Modeling quaternary geomorphic surfaces using laboratory, field, and imaging spectrometry in the lower Colorado Sonoran Desert : the Chameleon concept

Chameleon is a physics-based landscape modeling software system designed for modeling and simulations applications. Hyperspectral laboratory, field, and imaging spectrometer measurements are collected as empirical foundation data. Linear spectral unmixing is performed to decompose each image pixel into spectral endmembers. Mathematical manipulation of these fractional abundances and introduction of new spectral information is accomplished with spectral editing tools. Image spectra are modified on a sub-pixel, per-pixel, or neighborhood basis, or the entire hypercube can be customized at once. Chameleon then regenerates synthetic, but spectrally accurate, terrain models using linear spectral remixing algorithms. By incorporating elevation, sun angle, and weather data, the landscape becomes a "Chameleon"- able to change hyperspectral properties based on multitemporal spectral measurements, requirements for developmental tests and operational training, or as required by specific simulation scenarios. Advanced knowledge of natural environments to be modeled is prerequisite to generating useful synthetic terrains. Our spectral research on and Quaternary geomorphic surfaces suggests that deserts (often assumed to be less difficult to study remotely than humid, temperate, and cold environments) are more complex than is generally accepted. A variety of rock coatings can significantly alter reflectance in the solar reflected spectrum. Weathering rinds and carbonate deposits inhibit lithologic reflectance altogether. However, manganese-rich rock varnish obscures rock reflectance in the visible and near infrared wavelengths, but transmits lithologic information in the 2,000 to 2,500 nanometer (nm) wavelengths. Surface soils on desert pavements consist of a layer of eolian dust that overlies an accreting vesicular (Av) horizon. These soils have same structure and chemistry, and, therefore, the same hyperspectral signature, regardless of landform age, geomorphic process, or parent material. From a remote sensing perspective, this has a normalizing effect on reflectance across the landscape. Spectral Mixture Analysis is a proven hyperspectral technique for mapping composition and abundance of surface materials characteristic of volcanic landforms that exhibit diagnostic absorption features. We found that desert pavement spectra are featureless in that they exhibit few distinct spectral features related to rock varnish, clast lithology, or soil. Image spectra of these surfaces are the result of intimate mixtures of heterogeneous materials, requiring nonlinear spectral unmixing solutions