scholarly journals Optimized calibration strategy for high order adaptive optics systems in closed-loop: the slope-oriented Hadamard actuation

2015 ◽  
Vol 23 (21) ◽  
pp. 27134 ◽  
Author(s):  
Serge Meimon ◽  
Cyril Petit ◽  
Thierry Fusco
Keyword(s):  
Adaptive Optics ◽  
Closed Loop ◽  
High Order

scholarly journals 1178 J, 527 nm near diffraction limited laser based on a complete closed-loop adaptive optics controlled off-axis multi-pass amplification laser system

2021 ◽  
Vol 9 ◽  
Author(s):  
Deen Wang ◽  
Xin Zhang ◽  
Wanjun Dai ◽  
Ying Yang ◽  
Xuewei Deng ◽  
...  
Keyword(s):  
Laser Beam ◽  
Adaptive Optics ◽  
Closed Loop ◽  
Focal Spot ◽  
Beam Quality ◽  
Laser System ◽  
Diffraction Limit ◽  
Kdp Crystal ◽  
Main Amplifier

Abstract A 1178 J near diffraction limited 527 nm laser is realized in a complete closed-loop adaptive optics (AO) controlled off-axis multi-pass amplification laser system. Generated from a fiber laser and amplified by the pre-amplifier and the main amplifier, a 1053 nm laser beam with the energy of 1900 J is obtained and converted into a 527 nm laser beam by a KDP crystal with 62% conversion efficiency, 1178 J and beam quality of 7.93 times the diffraction limit (DL). By using a complete closed-loop AO configuration, the static and dynamic wavefront distortions of the laser system are measured and compensated. After correction, the diameter of the circle enclosing 80% energy is improved remarkably from 7.93DL to 1.29DL. The focal spot is highly concentrated and the 1178 J, 527 nm near diffraction limited laser is achieved.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

scholarly journals The PALM-3000 high-order adaptive optics system for Palomar Observatory

2008 ◽  
Author(s):  
Antonin H. Bouchez ◽  
Richard G. Dekany ◽  
John R. Angione ◽  
Christoph Baranec ◽  
Matthew C. Britton ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
High Order ◽  
Adaptive Optics System

Global fixed-time stabilization for a class of uncertain high-order nonlinear systems with dead-zone input nonlinearity

2018 ◽  
Vol 41 (7) ◽  
pp. 1888-1895
Author(s):  
Fangzheng Gao ◽  
Yanling Shang ◽  
Yuqiang Wu ◽  
Yanhong Liu
Keyword(s):  
Nonlinear Systems ◽  
State Feedback ◽  
Closed Loop ◽  
Dead Zone ◽  
Fixed Time ◽  
High Order ◽  
Closed Loop System ◽  
Sign Function ◽  
Input Nonlinearity ◽  
Power Integrator

This paper considers the problem of global fixed-time stabilization for a class of uncertain high-order nonlinear systems. One distinct characteristic of this work is that the system under consideration possesses the dead-zone input nonlinearity. By delicately combining the sign function with a power integrator technique, a state feedback controller is designed such that the states of the resulting closed-loop system converge to the origin within a fixed time. A simulation example is provided to illustrate the effectiveness of the proposed approach.

A Closed Loop Layer‐oriented Adaptive Optics Test Bed: Applications to Ground‐Layer Adaptive Optics

2007 ◽  
Vol 119 (860) ◽  
pp. 1114-1125 ◽  
Author(s):  
S. E. Egner ◽  
W. Gaessler ◽  
T. M. Herbst ◽  
R. Ragazzoni
Keyword(s):  
Adaptive Optics ◽  
Closed Loop ◽  
Ground Layer ◽  
Test Bed
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