基于IESPSO的舵机倾转矢量动力系统建模与参数辨识
Modeling and parameter identification of a steering gear tilting vector power system based on IESPSO
沈跃 1王佳俊 1储金城 1刘铭晖 1刘慧1
作者信息
- 1. 江苏大学电气信息工程学院 镇江 212013
- 折叠
摘要
针对在旋翼动态时产生的外力矩影响下,倾转矢量动力系统中的舵机系统模型辨识精度低、实际响应难以估计的问题,本文将舵机外力矩作为扰动噪声纳入辨识环节,构建系统模型,并提出了一种基于改进生态系统粒子群优化(IESPSO)的倾转旋翼舵机系统参数辨识方法.为确保试验稳定安全进行,本文设计了倾转矢量动力系统辨识平台,进行参数辨识试验.试验结果表明,在旋翼动态时产生的外力矩噪声影响下,IESPSO相对于粒子群优化法、生态系统粒子群优化法与递推最小二乘法,均方根误差降低了 1.46%,1.79%与 56.37%,辨识精度有明显提升,并具备更快的寻优收敛速度.在修改搜索空间后,IESPSO仍具有较高的寻优精度,避免了在宽搜索空间下无法快速搜索至较优可行解的问题.
Abstract
To solve the problem of low accuracy and difficulty in estimating the actual response of the servo system model in the tilt vector dynamic system under the influence of the external torque generated during rotor dynamics,this article incorporates the external moment of the servo as the disturbance noise into the identification link,constructs the system model,and proposes a parameter identification method for the tiltrotor servo system based on improving ecosystem particle swarm optimization(IESPSO).To ensure the stability and safety of the test,a tilt vector dynamical system identification platform was designed to carry out the parameter identification test.The experimental results show that the IESPSO method has obvious advantages of convergence speed and estimation accuracy compared with the PSO algorithm,the ESPSO algorithm,and the recursive least squares method under the influence of the external moment noise generated during rotor dynamics.
关键词
系统辨识/生态系统粒子群/舵机系统模型/倾转矢量动力系统Key words
system identification/ecosystem particle swarm/steering gear model/tilting vector power system引用本文复制引用
基金项目
中国高校产学研创新基金无人集群协同智能项目(2021ZYB02002)
出版年
2024
NETL
NSTL
万方数据