[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 基于改进果蝇算法的涡轴发动机状态变量模型建立方法-State Variable Model Establishment of Turbo Shaft Engine SVM Based on Improved Fruit Fly Algorithm
文章摘要
贾伟州1,谢寿生1,彭靖波1,王磊1,刘云龙2.基于改进果蝇算法的涡轴发动机状态变量模型建立方法[J].空军工程大学学报:自然科学版,2019,20(2):13-20
基于改进果蝇算法的涡轴发动机状态变量模型建立方法
State Variable Model Establishment of Turbo Shaft Engine SVM Based on Improved Fruit Fly Algorithm
  
DOI:10.3969/j.issn.1009-3516.2019.02.003
中文关键词: 航空发动机  状态变量模型  混沌映射  改进果蝇算法  LQ/H∞抗扰控制器
英文关键词: aircraft engine  state variable model  chaos map  modified fruit fly optimization algorithm  LQ/H∞ disturbance-rejection controller
基金项目:国家自然科学基金(51606219;51506221)
作者单位
贾伟州1,谢寿生1,彭靖波1,王磊1,刘云龙2 1.空军工程大学航空工程学院,西安,710038
2.空军石家庄飞行学院,石家庄,050073 
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中文摘要:
      针对拟合法在航空发动机小偏差状态变量模型建立中受系统模态及模型阶次的限制,提出一种基于改进果蝇优化算法(MICFOA)建立小偏差状态变量模型的方法。首先,将该方法分为2个子过程:先优化系统矩阵和输入矩阵并找到最优结果,再对输出矩阵和传输矩阵优化;同时根据状态变量模型与非线性模型动态响应一致构造了不受变量值域影响的适应度函数。其次,在果蝇优化算法(FOA)中引入协同子种群策略和混沌映射策略来增强迭代寻优中种群多样性,引入自适应调整策略来平衡全局搜索与局部搜索的关系,避免算法早熟收敛。最后应用上述方法建立了涡轴发动机小偏差状态变量模型,并设计了LQ/H∞抗扰控制器。仿真结果表明:MICFOA相比FOA能提高5~10个数量级的精度,且所建模型与非线性模型吻合一致,具有良好的动静态性能。
英文摘要:
      Aimed at the problems that the fitting method is limited by the system model and the model order in establishing a small deviation state variable model of aircraft engine, a method based on multiple-improved-chaotic fruit fly optimization algorithm (MICFOA) is proposed. Firstly, the method is divided into two sub-processes: first thing is to optimize the system matrix, input matrix, and to find the optimal results, and then is to optimize the output matrix and the transfer matrix. Simultaneously, work is done according to the principle that the SVM's dynamic response is consistent with the nonlinear model's, fitness functions are constructed that are unaffected by the variable value domain. Secondly, synergisitic sub-population strategy and chaos mapping strategy are introduced into FOA to improve the diversity of fruit fly populations by using the adaptive adjustment strategy introduced to balance the relationship between global search and local search to avoid premature convergence. Finally this method is used to establish a turbo-shaft engine's SVM, and to design LQ/H∞ disturbance-rejection controller. The simulation results show that MICFOA can improve the accuracy of 5-10 orders of magnitude compared with FOA, the SVM has good dynamic and static performance, and the newly built model is consistent with the nonlinear model.
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