Abstract
Aiming at lower startup power consumption, stronger thermal load adaptability, easier parameters adjustment, and higher parameter tuning efficiency for the temperature control system of a distributed Bragg reflector (DBR) semiconductor laser, this paper employs the double-loop control and intelligent parameter tuning methods. First, the thermal equivalent circuit model is established for the laser temperature control system, which has stronger thermal load adaptability than the traditional transfer function model. In order to improve the modeling speed and accuracy, a mean impact value (MIV) quantum particle swarm optimization (QPSO) intelligent algorithm is proposed to tune the model parameters. A double-loop temperature control system is set up on this basis. Then, the MIV-QPSO intelligent algorithm is used to tune the control parameters, which shortens the settling time, increases the tuning efficiency, and improves the temperature control effect. The feasibility and effectiveness of the proposed methods are verified through the MATLAB/Simulink simulation of the laser temperature control process.
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