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1	Self-Smoothing Functional Estimation Yake, Bronson Thomas 13 December 2002 (has links) Analysis of measured data is often required when there is no deep understanding of the mathematics that accurately describes the process being measured. Additionally, realistic estimation of the derivative of measured data is often useful. Current techniques of accomplishing this type of data analysis are labor intensive, prone to significant error, and highly dependent on the expertise of the engineer performing the analysis. The ?Self-Smoothing Functional Estimation? (SSFE) algorithm was developed to automate the analysis of measured data and to provide a reliable basis for the extraction of derivative information. In addition to the mathematical development of the SSFE algorithm, an example is included in Chapter III that illustrates several of the innovative features of the SSFE and associated algorithms. Conclusions are drawn about the usefulness of the algorithm from an engineering perspective and additional possible uses are mentioned. trajectory estimation filter curve fit
2	Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control Hakala, Tim 31 January 2006 (has links) (PDF) A new method of adaptive impulse control is developed to precisely and quickly control the position of machine components subject to friction. Friction dominates the forces affecting fine positioning dynamics. Friction can depend on payload, velocity, step size, path, initial position, temperature, and other variables. Control problems such as steady-state error and limit cycles often arise when applying conventional control techniques to the position control problem. Studies in the last few decades have shown that impulsive control can produce repeatable displacements as small as ten nanometers without limit cycles or steady-state error in machines subject to dry sliding friction. These displacements are achieved through the application of short duration, high intensity pulses. The relationship between pulse duration and displacement is seldom a simple function. The most dependable practical methods for control are self-tuning; they learn from online experience by adapting an internal control parameter until precise position control is achieved. To date, the best known adaptive pulse control methods adapt a single control parameter. While effective, the single parameter methods suffer from sub-optimal settling times and poor parameter convergence. To improve performance while maintaining the capacity for ultimate precision, a new control method referred to as Adaptive Impulse Control (AIC) has been developed. To better fit the nonlinear relationship between pulses and displacements, AIC adaptively tunes a set of parameters. Each parameter affects a different range of displacements. Online updates depend on the residual control error following each pulse, an estimate of pulse sensitivity, and a learning gain. After an update is calculated, it is distributed among the parameters that were used to calculate the most recent pulse. As the stored relationship converges to the actual relationship of the machine, pulses become more accurate and fewer pulses are needed to reach each desired destination. When fewer pulses are needed, settling time improves and efficiency increases. AIC is experimentally compared to conventional PID control and other adaptive pulse control methods on a rotary system with a position measurement resolution of 16000 encoder counts per revolution of the load wheel. The friction in the test system is nonlinear and irregular with a position dependent break-away torque that varies by a factor of more than 1.8 to 1. AIC is shown to improve settling times by as much as a factor of two when compared to other adaptive pulse control methods while maintaining precise control tolerances. control position adaptive impulsive settling-time nonlinear friction pulses displacements precise tolerances log-spaced update distributed learning Coulomb Stribeck Tomizuka Yang AIC PID MRAC STR RTAI Linux FreeBSD kernel modules microcontroller convergence practical self-tuning methods techniques limit-cycles steady-state error zero stable stability bound envelope partitioned scheme lookup-table multi-point adaptation repeatable mean servo motor exponential square-law rise-time real-time log-log interpolation pro-forma curve-fit sensitivity compliance variable static dynamic response torque acceleration velocity optical encoder parameters evolution fixed-law enhanced split weighting initialization trajectory layered processes Mechanical Engineering

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Self-Smoothing Functional Estimation

Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control