Browsing by Subject "System identification"
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Item Algorithms in system identification(Texas Tech University, 1988-08) Shaukat, Aamer SohailA first set of FORTRAN programs was written that generates noisy input output data from a multi-input, multi-output, linear, time-invariant system. A random number generator is used to produce the output noise. The system parameters are identified from the noisy input-output data using an adaptation of the least squares method in the second set of programs. The third set presents a new algorithm for system parameter identification using the concept of the supremal [F, G]-invariant subspace in ker[H], and some results from optimal control theory. Most of the results are calculated and tabulated for a specific two-input, two-output, third order system.Item Control strategies and motion planning for nanopositioning applications with multi-axis magnetic-levitation instruments(Texas A&M University, 2007-09-17) Shakir, HuzefaThis dissertation is the first attempt to demonstrate the use of magnetic-levitation (maglev) positioners for commercial applications requiring nanopositioning. The key objectives of this research were to devise the control strategies and motion planning to overcome the inherent technical challenges of the maglev systems, and test them on the developed maglev systems to demonstrate their capabilities as the next-generation nanopositioners. Two maglev positioners based on novel actuation schemes and capable of generating all the six-axis motions with a single levitated platen were used in this research. These light-weight single-moving platens have very simple and compact structures, which give them an edge over most of the prevailing nanopositioning technologies and allow them to be used as a cluster tool for a variety of applications. The six-axis motion is generated using minimum number of actuators and sensors. The two positioners operate with a repeatable position resolution of better than 3 nm at the control bandwidth of 110 Hz. In particular, the Y-stage has extended travel range of 5 mm ???? 5 mm. They can carry a payload of as much as 0.3 kg and retain the regulated position under abruptly and continuously varying load conditions. This research comprised analytical design and development, followed by experimental verification and validation. Preliminary analysis and testing included open-loop stabilization and rigorous set-point change and load-change testing to demonstrate the precision-positioning and load-carrying capabilities of the maglev positioners. Decentralized single-input-single-output (SISO) proportional-integral-derivative (PID) control was designed for this analysis. The effect of actuator nonlinearities were reduced through actuator characterization and nonlinear feedback linearization to allow consistent performance over the large travel range. Closed-loop system identification and order-reduction algorithm were developed in order to analyze and model the plant behavior accurately, and to reduce the effect of unmodeled plant dynamics and inaccuracies in the assembly. Coupling among the axes and subsequent undesired motions and crosstalk of disturbances was reduced by employing multivariable optimal linear-quadratic regulator (LQR). Finally, application-specific nanoscale path planning strategies and multiscale control were devised to meet the specified conflicting time-domain performance specifications. All the developed methodologies and algorithms were implemented, individually as well as collectively, for experimental verification. Some of these applications included nanoscale lithography, patterning, fabrication, manipulation, and scanning. With the developed control strategies and motion planning techniques, the two maglev positioners are ready to be used for the targeted applications.Item Detection and transient dynamics modeling of experimental hypersonic inlet unstart(2011-12) Hutchins, Kelley Elizabeth; Akella, Maruthi Ram, 1972-; Clemens, Noel T.During unstart, the rapid upstream propagation of a hypersonic engine's inlet shock system can be clearly seen through inlet pressure measurements. Specifically, the magnitude of the pressure readings suddenly and dramatically increases as soon as the leading edge of the shock system passes the measurement location. A change detection algorithm can monitor the pressure time history at a given sensing location and determine when an abrupt pressure rise occurs. If this kind of information can be obtained at various sensing locations distributed throughout the inlet then a feedback control scheme has an improved basis upon which to make actuation decisions for preventing unstart. In this thesis a variety of change detection algorithms have been implemented and tested on multiple sources of experimental high-speed pressure transducer data. The performance of these algorithms is compared and suitability of each algorithm for the general unstart problem is discussed. Attempts to model the transient dynamics governing the unstart process have also been made through the use of system identification techniques. The result of these system identification efforts is a partially nonlinear mathematical model that describes shock motion through pressure signals. The process reveals that the nonlinear behavior can be separated from the linear with relative ease. Related attempts are then made to create a model where the nonlinear portion has been specified leaving only the linear portion to be determined by system identification. The modeling and identification process specific to the unstart data used is discussed and successful models are presented for both cases.Item Development and validation of a system identification methodology for the characterization of contaminated sites(Texas Tech University, 1998-12) Jayakody, Kankanamalage Geethani KumariThe quality of groundwater has become a major concern in the United States and many other industrialized countries since the discovery of numerous sites with contamination from hazardous wastes and leaked fuels. Investigation and monitoring have begun at many of these sites, but execution of remedial plans has often been delayed due to regulatory and financial constraints as well as limited understanding of the processes that control the distribution of contaminants in the subsurface. One of the primary difficulties encountered in the site remediation process is the inability to determine the site heterogeneity in an adequate manner. In this research, a mathematical technique known as System Identification Methodology (SID) is used in conjunction with the flow and contaminant transport equations to address the above problem. In this mathematical procedure, a finite volume formulation with an upwind velocity scheme was used to discretize the flow and transport equations. In order to determine site heterogeneity, independent contaminant transport parameters were assigned to each rectangular element in the flow domain. The finite volume formulations of groundwater flow and contaminant transport equations were then reorganized into the standard state-space form that is commonly used in system identification procedures. The determination of the distribution of unknown parameters at the site was then accomplished by minimizing the error between both measured contaminant concentration and hydraulic head and calculated contaminant concentration and hydraulic head by the above finite volume models for groundwater flow and contaminant transport. The Levenberg-Marquardt algorithm was used as the optimization scheme. Once the mathematical model was developed, test cases were run to verify the mathematical accuracy of the model. Sensitivity analyses were performed to determine the relative significance of the heterogeneities in dispersion, retardation, and decay terms on contaminant flow. The model was validated by applying it to actual observations from four selected case studies. As the first step in the validation, the SID methodology was applied to contaminant concentration data obtained from large-scale sand tank tests. Subsequently field validation was accomplished by applying the SID methodology to site in the Southern High Plains of Texas, Jordan aquifer in Iowa and landfill site at Borden.Item A novel subspace identification algorithm and its application in stochastic fault detection(2004) Wang, Jin; Qin, S. Joe