基于改进灰狼算法的LQR优化控制方法研究
Research on LQR Optimal Control Method Based on Improved Gray Wolf Algorithm
宋涛涛 1李艳萍 1李洪港1
作者信息
- 1. 山东建筑大学信息与电气工程学院,山东 济南 250101
- 折叠
摘要
针对二级倒立摆在使用LQR(线性二次调节器)进行优化控制过程中,由经验选取的加权矩阵Q和R参数存在着较大的随机性和不稳定性问题,提出了改进灰狼算法优化控制器加权矩阵Q和R的方法.为灰狼算法设计了基于二次余弦规律的自适应收敛因子a和增强α狼适应度值fα 的比例权重方法.增强了算法迭代前期的全局搜索能力和后期的收敛速度,通过MATLAB/Simulink仿真,并与传统灰狼算法相比较,得出改进算法能够有效降低倒立摆回归平衡状态时的超调量,更快达到稳定状态,使控制效果更加理想.
Abstract
Aiming at the problem of significant randomness and instability in the weighting matrix Q and R param-eters selected by experience during the optimization control process using LQR(linear quadratic regulator)for a two-stage inverted pendulum,an improved grey wolf algorithm is proposed to optimize the controller weighting matrix Q and R.An adaptive convergence factor a based on quadratic cosine and a proportionalα weight method to enhance wolf fitness value fα were designed for Gray Wolf algorithm.The global search ability in the early stage of the algo-rithm iteration and the convergence speed in the later stage were enhanced,and the optimal weighting matrix Q and R parameters were obtained.By MATLAB/Simulink simulation,the improved algorithm can effectively reduce the over-shoot when the inverted pendulum returns to the balance state,reach the stable state faster and achieve better control effect compared with the traditional gray wolf algorithm.
关键词
灰狼算法/线性二次调节器/二级倒立摆/收敛因子/适应度值Key words
Grey algorithm/Linear quadratic regulator/Double inverted pendulum/Convergence factor/Fitness引用本文复制引用
出版年
2024
NETL
NSTL
万方数据