Online calibration for FBG networks based on improved particle swarm optimization algorithm

It is of great significance to perform FBG sensor calibration in shape reconstruction for a board. It impacts on the shape reconstruction accuracy directly. Aiming at the deficiencies of traditional off-line calibration method, an online calibration for FBG networks based on improved particle swarm optimization algorithm was put forward, optimization model was established, and fitness function was given. The optimal goal is to obtain the minimum of fitness function. Improved particle swarm optimization algorithm was used to obtain the optimal solution, ie. strain-curvature coefficients of FBG networks. The simulation experiments verified the effectiveness of the method. Finally, experimental platform was built for experimental verification and analysis of the results of calibration. Experimental results showed that high reconstruction accuracy can be obtained by this calibration method, while the measurement characteristics of the FBG sensors can be reflected more accurately.