scholarly journals On-sky results for adaptive optics control with data-driven models on low-order modes

2020 ◽  
Vol 498 (3) ◽  
pp. 3228-3240
Author(s):  
Baptiste Sinquin ◽  
Léonard Prengère ◽  
Caroline Kulcsár ◽  
Henri-François Raynaud ◽  
Eric Gendron ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
High Performance ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Integral Action ◽  
High Performance Systems ◽  
Performance Results ◽  
Low Order

ABSTRACT Dedicated tip–tilt loops are commonly implemented on adaptive optics (AO) systems. In addition, a number of recent high-performance systems feature tip–tilt controllers that are more efficient than the integral action controller. In this context, linear–quadratic–Gaussian (LQG) tip–tilt regulators based on stochastic models identified from AO telemetry have demonstrated their capacity to effectively compensate for the cumulated effects of atmospheric disturbance, windshake and vibrations. These tip–tilt LQG regulators can also be periodically retuned during AO operations, thus allowing to track changes in the disturbances’ temporal dynamics. This paper investigates the potential benefit of extending the number of low-order modes to be controlled using models identified from AO telemetry. The global stochastic dynamical model of a chosen number of turbulent low-order modes is identified through data-driven modelling from wavefront sensor measurements. The remaining higher modes are modelled using priors with autoregressive models of order 2. The loop is then globally controlled using the optimal LQG regulator build from all these models. Our control strategy allows for combining a dedicated tip–tilt loop with a deformable mirror that corrects for the remaining low-order modes and for the higher orders altogether, without resorting to mode decoupling. Performance results are obtained through evaluation of the Strehl ratio computed on H-band images from the scientific camera, or in replay mode using on-sky AO telemetry recorded in 2019 July on the CANARY instrument.

scholarly journals Experimental study on modified linear quadratic Gaussian control for adaptive optics

2014 ◽  
Vol 53 (8) ◽  
pp. 1610 ◽  
Author(s):  
Qiang Fu ◽  
Jörg-Uwe Pott ◽  
Diethard Peter ◽  
Feng Shen ◽  
Changhui Rao ◽  
...  
Keyword(s):  
Experimental Study ◽  
Adaptive Optics ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian

Automatic disturbance identification for linear quadratic Gaussian control in adaptive optics

2020 ◽  
Vol 496 (4) ◽  
pp. 5126-5138
Author(s):  
Jiaying Wang ◽  
Youming Guo ◽  
Lin Kong ◽  
Lanqiang Zhang ◽  
Naiting Gu ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Measurement Data ◽  
Pso Algorithm ◽  
Solar Observatory ◽  
Automatic Identification ◽  
Separation Procedure ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Identification Method ◽  
Maximum Likelihood Technique

ABSTRACT Linear quadratic Gaussian (LQG) control is an appealing control strategy to mitigate disturbances in adaptive optics (AO) systems. The key of this method is to quickly and consecutively build an accurate dynamical model to track time-varying disturbances such as turbulence, wind load and vibrations. In order to address this problem, we propose an automatic identification method consisting mainly of an improved spectrum separation procedure and a parameter optimization process based on the particle swarm optimization (PSO) algorithm. The improved spectrum separation can pick out perturbation peaks more accurately, especially when some peaks are very close together. Moreover, compared with the Levenberg–Marquardt method and the maximum-likelihood technique based on grids, the PSO algorithm has a faster convergence speed and lower computational burden, and thus is easier to implement. The entire identification process can run automatically online without human intervention. This identification method is verified with a synthetic disturbance profile in a simulation. Furthermore, the performance of the method is evaluated with consecutive measurement data recorded by the 1-m New Vacuum Solar Telescope at the Fuxian Solar Observatory.

A data-driven predictive controller combined with the vector autoregressive with exogenous input model and the propagator estimation method for vehicle lateral stabilization

2020 ◽  
Vol 235 (1) ◽  
pp. 94-116
Author(s):  
Weishun Deng ◽  
Weimiao Yang ◽  
Jianwu Zhang ◽  
Pengpeng Feng ◽  
Fan Yu
Keyword(s):  
Model Identification ◽  
Stability Control ◽  
Data Driven ◽  
Vehicle Stability ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Vector Autoregressive ◽  
Vehicle Stability Control ◽  
Predictive Controller ◽  
Input Model

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions.

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