基于MRAS-DCQGA算法的空气静压轴承伺服系统辨识及参数整定

Identification and Parameter Tuning of the Aerostatic Bearing Servo System Based on the MRAS-DCQGA

为了提高空气静压轴承伺服系统的控制性能,这里基于模型参考自适应系统(MRAS)设计了辨识算法,并应用双链量子遗传算法(DCQGA)对自适应律的常数项及PI调节器参数进行了优化。仿真结果表明,与传统的遗传算法参数优化对比,辨识结果在合理的误差范围内,然而DCQGA算法表现出稳定的动态特性和全局搜索能力。因此,MRAS-DCQGA算法在保证辨识精度的情况下减轻了人工整定参数的工作量,具有重要的工程意义。

In order to improve the performance of the aerostatic bearing servo system,the identification algorithm was established based on the model reference adaptive system(MRAS),and the double chains quantum genetic algorithm(DCQGA)was employed to optimize the parameters of the proportional integral regulators and the constant parameters in the adaptive law.Simulation results indicate that,compared with the parameters optimization by traditional genetic algorithm,the DCQGA represents stable dynamic characteristics and desirable global searching ability within a reasonable tolerance.Consequently,the MRAS-DCQGA algorithm can not only promise the accuracy of the identification,but also alleviate the burden of engineer,which is of great significance to the engineering.