摘要
阐述针对LQG控制器用于半主动悬架存在加权系数依靠人为调节的问题,提出一种基于改进粒子群算法的LQG控制方法。该算法根据适应度函数值动态调整惯性权重,通过tanh激活函数动态调整学习因子,提高其全局搜索能力和收敛速度,求得更具有适应性的LQG控制矩阵系数,仿真验证其有效性。
This paper describes the problem that the weighting coefficient of LQG controller used in semi-active suspension depends on manual adjustment,and a LQG control method based on improved particle swarm optimization algorithm.The algorithm dynamically adjusts the inertia weight according to the fitness function value,and dynamically adjusts the learning factor through the tanh activation function,which improves its global search ability and convergence speed,and obtains the more adaptive LQG control matrix coefficient.The simulation verifies its effectiveness.
作者
张娜
宁佳明
赵强
姜鸿瑞
ZHANG Na;NING Jiaming;ZHAO Qiang;JIANG Hongrui(College of Electrical and Control Engineering,Heilongjiang University of Science and Technology,Heilongjiang 150022,China;Harbin Engineering University,School of Computer Science and Technology,Heilongjiang 150001,China;College of Electrical and Mechanical Engineering,Northeast Forestry University,Heilongjiang 150040,China)
出处
《电子技术（上海）》
2024年第4期369-371,共3页
Electronic Technology
基金
2018年黑龙江科技大学引进高层次人才科研启动基金项目,2020年黑龙江省普通高校青年创新人才培养计划项目(UNPYSCT-2020034)
2022年度黑龙江省省属本科高校基本科研业务费青年创新人才培育计划项目(2022-KYYWF-0561)
黑龙江省重点研发项目(JD22A014)。
关键词
半主动悬架
不同路面等级
粒子群算法
LQG控制
semi-active suspension
different road levels
particle swarm optimization algorithm
LQG control
