Beam jitter model parameter real-time identification method based on particle swarm algorithm

Abstract

The invention discloses a beam jitter model parameter real-time identification method based on a particle swarm algorithm, and the method comprises the following steps: firstly, building a beam jittermodel; secondly, collecting a light beam jitter time sequence signal; thirdly, determining an identification error criterion function; and then, carrying out global random search on the parameters ofthe light beam jitter model by utilizing a particle swarm algorithm so as to obtain the optimal parameters of the model, thereby realizing intelligent optimization of the model parameters. Compared with an existing method, the method has the most remarkable characteristics of random initial value, high calculation speed and high real-time performance. Moreover, in the iteration process, through self-adaptive change of an inertia weight factor, the ability of the algorithm to jump out of local convergence is greatly improved, and meanwhile, the efficiency of the algorithm can be improved. In addition, the method does not need an additional auxiliary system or manual debugging, and is low in implementation cost; and the method can be used for identifying the parameters of the light beam jitter model in different systems, and is high in transportability.

Description

technical field [0001] The invention relates to the technical field of beam jitter model parameter identification, in particular to a method for real-time identification of beam jitter model parameters based on a particle swarm algorithm. Background technique [0002] Beam jitter is a phenomenon in which the optical axis of the beam is deflected and the propagation direction of the beam is constantly changing. In adaptive optics systems or high-energy laser systems, the high-frequency narrow-band beam jitter caused by the system platform or vibration devices greatly limits the improvement of system performance. How to effectively suppress the jitter of high-frequency narrow-band beams to further improve system performance has become an urgent problem to be solved. [0003] Scholars at home and abroad have studied the suppression of high-frequency narrow-band beam jitter from many aspects. Among them, the linear quadratic Gaussian LQG (Linear Quadratic Gaussian) control algo...

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Problems solved by technology

Among these methods, the generalized kalman filter method is based on a nonlinear observer and has poor fitting effect on high-frequency signals; the maximum likelihood method is limited by prior information; the damped Gauss-Newton method and the forecast error method are sensitive to initial values, and the amount of calculation is relatively large. Large and requires more human participation in debugging, which is not conducive to real-time online identification of model parameters

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