Laser Beam Jitter Control Using Recursive-Least-Squares Adaptive Filters

Author(s):  
Hyungjoo Yoon ◽  
Brett E. Bateman ◽  
Brij N. Agrawal

The primary focus of this research is to develop and implement control schemes for combined broadband and narrowband disturbances to optical beams. The laser beam jitter control testbed developed at the Naval Postgraduate School is used for development of advanced jitter control techniques. First, we propose a least quadratic Gaussian feedback controller with integrator for cases when only the error signal (the difference between the desired and the actual beam positions) is available. An anti-notch filter is also utilized to attenuate a vibrational disturbance with a known frequency. Next, we develop feedforward adaptive filter methods for cases when a reference signal, which is highly correlated with the jitter disturbance, is available. A filtered-X recursive least-squares algorithm with an integrated bias estimator is proposed to deal with a constant bias disturbance. Finally, experimental results are provided to validate and compare the performance of the developed control techniques. The designed adaptive filter has a simple structure but shows good jitter rejection performance, thanks to the use of a reference signal.

1994 ◽  
Vol 35 (3) ◽  
pp. 231-239 ◽  
Author(s):  
Bengt Carlsson ◽  
Peter Händel

1991 ◽  
Vol 71 (3) ◽  
pp. 1159-1165 ◽  
Author(s):  
A. M. Lauzon ◽  
J. H. Bates

Continuous estimation of time-varying respiratory mechanical parameters is required to fully characterize the time course of bronchoconstriction. To achieve such estimation, we developed an estimator that uses the recursive linear least-squares algorithm to fit the equation Ptr = RV + EV + K to measurements of tracheal pressure (Ptr) and flow (V). The volume (V) is obtained by numerical integration of V. The estimator has a finite memory with length into the past at each point in time that varies inversely with the difference between the current measurement of Ptr and that predicted by the model, to allow the algorithm to track rapidly varying parameters (R, E, and K). V usually exhibits significant drift and must be corrected. Of the several correction methods investigated, subtraction of the recursively weighted average of V before integration to V was found to perform best. The estimator was tested on simulated noisy data where it successfully followed a fivefold increase in R and a twofold increase in E occurring over 10 s. Three dogs and two cats were anesthetized, paralyzed, tracheostomized, and challenged with a bolus of methacholine (approximately 13 mg/kg iv). Increases of 3- to 10-fold were observed in R and 2- to 3-fold in E, beginning within 10–40 s after the bolus injection. In some animals we found that the increase in E occurred more slowly than that in R, which the V signal suggested was due to dynamic hyperinflation of the lungs. These results demonstrate that our recursive estimator is able to track rapid changes in respiratory mechanical parameters during bronchoconstrictor challenge.


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