改进的BBO算法在PID参数整定中的应用

Application of Improved BBO Algorithm in PID Parameter Tuning

为了提高比例积分微分(Proportion integration differentiation,PID)控制器的控制性能,同时解决人工调试PID参数困难。文章提出了一种改进的生物地理学优化算法(Chaos Biogeography-based optimization,CBBO),通过嵌入混沌映射,同时引入余弦迁移模型,改善BBO算法在前期易早熟、后期搜索能力不强等缺点。将改进后的算法采用经典测试函数进行验证,并和其他群智能算法进行比较,结果表明CBBO的收敛速度更快,搜索精度更高。最后,把CBBO算法应用到PID参数整定中,仿真算例结果表明,采用CBBO整定参数后的PID控制器误差更小、超调量更小、抗干扰能力更强。

In order to improve the control performance of the Proportion Integration Differentiation(PID) controller, this paper proposes an improved biogeography-based optimization(CBBO) algorithm. By embedding chaotic map and introducing cosine migration model, improving the shortcomings of the BBO algorithm that is easy to premature and search capability is poor in late. The improved algorithm is verified by classical test function and compared with other cluster intelligent algorithms. The results show that CBBO has faster convergence speed and higher search accuracy. Finally, the CBBO algorithm is applied to the PID parameter tuning. The simulation results show that the PID controller with CBBO tuning parameters has smaller error, smaller overshoot and stronger anti-interference ability.