摘要
自适应光学系统的性能受限于伺服系统的延迟误差和波前传感器的光电子噪声。提出了一种多模型单变量预测模型,该模型采用基于Levenberg-Marquardt学习算法的前馈型神经网络。利用计算机多核处理器,设计了一个具有并行处理能力的预测控制器,来实现对自适应光学闭环控制电压的预测,以消除延迟误差的影响。通过数值仿真实验,研究了预测控制器对控制电压和远场斯特雷尔比的影响,与未采用预测控制器的系统进行了比较,并对预测算法的并行性能进行了分析。实验结果表明,使用并行化方法的预测控制器可以有效缩短系统的预测时间,提高预测算法的加速比,与经典比例积分(PI)控制算法相比可以更有效地降低系统由于伺服延迟引起的误差,远场的斯特雷尔比有明显地提高。
Performance of adaptive optics （AO） system is limited by the delay errors caused by the servo system and photoelectron noise at the wavefront sensor. A multi-model univariate prediction model is proposed, which is based on the two-layer back propagation neural network with Levenberg-Marquardt learning algorithm. Using the multi-core processors, a novel predictive controller with parallel processing capabilities is designed that is able to predict the control voltage in the closed-loop AO system and eliminate the delay errors. Through numerical simulation, the prediction performance and parallel efficiency are studied. The control voltages of the A0 system and the Strehl ratio are calculated and compared for the multi-model univariate prediction algorithm and proportional integral （PI） control algorithm. The results show that the residual error caused by servo delay in the system and Strehl ratio are improved effectively by using the predictive controller than by using the PI control algorithm. The prediction time is reduced by using multi-model univariate prediction algorithm.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2012年第8期26-36,共11页
Acta Optica Sinica
基金
国家自然科学基金(60978050
91118001)
国家973项目(2011CB302402)
四川省青年科技基金(09ZQ026-014)资助课题
关键词
大气光学
自适应光学
预测控制器
多模型单变量预测模型
并行化
多核
神经网络
atmospheric optics
adaptive optics
predictive controller
multi-model univariate prediction model
parallelization
multi-core
neural network
