融合改进人工蜂群的UKF算法研究  

Research on UKF Algorithm Integrating the Improved Artificial Bee Colony Algorithm

作　　者：刘建娟[1,2] 李志伟 姬淼鑫[1,2] 吴豪然 李浩 LIU Jianjuan;LI Zhiwei;JI Miaoxin;WU Haoran;LI Hao(School of Electrical Engineering Henan University of Technology,Zhengzhou 450000 China;Institute of Electromechanical Equipment and Measurement&Control Technology,Henan University of Technology,Zhengzhou 450000 China)

机构地区：[1]河南工业大学电气工程学院,郑州450000 [2]河南工业大学机电设备及测控技术研究所,郑州450000

出　　处：《电光与控制》2024年第11期10-17,共8页Electronics Optics & Control

基　　金：国家自然科学基金(62201199);河南省科技攻关项目(232102320037);河南工业大学自科创新基金支持计划项目(2021ZK CJ07)。

摘　　要：针对无迹卡尔曼滤波(UKF)算法在状态估计时异常系统噪声协方差矩阵影响滤波性能的问题,提出一种利用改进人工蜂群优化UKF的算法。首先,在UKF算法过程中引入IABC算法对系统噪声协方差矩阵寻优选择,从而实现自适应调节系统噪声协方差矩阵,提高估计精度;其次,对传统ABC算法采用Circle混沌初始化策略,增加人工蜂群初始种群的多样性;同时采用偏好随机游动策略,平衡算法的开发与探索能力,增强算法的稳定性;最后,通过动态扰动因子策略增强算法后期寻找最优解的能力,提高收敛速度,进一步优化算法性能。实验结果表明,相较于ABC算法,IABC算法在寻优性能上有明显提升。同时,通过对比UKF算法和IABC-UKF算法,验证了IABC-UKF算法的可行性,其位置均方根误差不大于1.4 m,表明该算法滤波效果较好且误差波动小,能够有效提高估计精度。When using Unscented Kalman Filter(UKF)algorithm for state estimation the abnormal system noise covariance matrix may affect the filtering performance.To address the problem a method utilizing the Improved Artificial Bee Colony Optimized UKF(IABC-UKF)algorithm is proposed.Firstly IABC is introduced into the UKF algorithm to optimize the selection of the system noise covariance matrix thus to achieve adaptive adjustment of the system noise covariance matrix and improve estimation accuracy.Secondly a Circle chaos initialization strategy is applied to the traditional ABC algorithm to increase the diversity of the initial population of artificial bee colony.Additionally a preference random walk strategy is employed to balance the algorithm s exploitation and exploration capabilities enhancing algorithm stability.Finally a dynamic perturbation factor strategy is used to enhance the algorithm s ability to find the optimal solution in the later stages improving convergence speed and further optimizing algorithm performance.Experimental results demonstrate that compared with the ABC algorithm IABC algorithm has a significant improvement in optimization performance.Furthermore a comparison between the UKF algorithm and the IABC-UKF algorithm confirms the feasibility of the IABC-UKF algorithm.With a root mean square error in position not exceeding 1.4 meters IABC-UKF algorithm exhibits good filtering performance with low error fluctuations which can effectively enhance the estimation accuracy.

关 键 词：无迹卡尔曼滤波 系统噪声协方差矩阵 人工蜂群算法 偏好随机游动 动态扰动因子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN713[自动化与计算机技术—控制科学与工程]