﻿ 基于AMESim对波浪控制平台电液伺服系统仿真与优化
 舰船科学技术  2016, Vol. 38 Issue (11): 70-74 PDF

1. 江苏科技大学 机械工程学院，江苏 镇江 212003 ;
2. 江苏科技大学 能源与动力工程学院，江苏 镇江 212003

Simulation and optimization of electro hydraulic servo system for wave control platform Based on AMESim
SHE Jian-guo1, QIU Guang-ting1, GE Jian-fei1, CHEN Ning2
1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China ;
2. School of Energy and Power Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Abstract: This paper took the wave compdded the transfer function for the its Critical hydraulic components, establishing the transfer function of electro hydraulic servo control system.Then we established the Hydraulic system model using AMESIM and explored the dynamic error under different servo gain parameters. The simulation results show that the system cannot meet the actual accuracy requirements through changing the servo gain only. So, we adopted PD control algorithm in the control system. By means of genetic algorithm in AMEsim optimization, the optimal Kp and Kd parameters are obtained.The result indicates that the use of PD control algorithm can make the system achieving the requirements of accuracy and stability.
Key words: AMESIM     hydraulic servo system     dynamic performance
0 引言

1 电液伺服控制系统

 图 1 船舶6个自由度运动 Fig. 1 The ship's motion of six freedom degree

 图 2 三自由度波浪补偿控制平台结构 Fig. 2 The structure of three degree freedom wave compensation platform

2 控制系统原理及传递函数建立 2.1 控制系统的组成与原理

 图 3 控制系统的组成原理 Fig. 3 The composition of control system
2.2 电液伺服系统数学模型

 ${Q_l} = {C_{ie}}{P_L} + {C_f}{P_s} + \left( {{V_t}/4{\beta _e}} \right){\dot P_L} + {A_p}\dot x{\text{。}}$ (1)

 ${Q_l} = {K_q}{x_v} - {K_{ce}}{P_L}{\text{。}}$ (2)

 $Y\left( S \right) = \frac{{\frac{{{K_q}}}{{{A_q}}}{x_v} - \frac{{{K_{ce}}}}{{{A^2}_p}}\left( {1 + \frac{{{V_t}}}{{4{\beta _e}{K_{ce}}}}} \right){F_L}}}{{s\left( {\frac{{{s^2}}}{{{\omega ^2}h}} + \frac{{2{\zeta _h}}}{{{\omega _h}}} + 1} \right)}}{\text{，}}$ (3)

 ${\omega _h} = 2\sqrt {\frac{{{\beta _e}{A^2}_p}}{{{M_t}{V_t}}}} {\text{，}}$ (4)
 ${\zeta _h} = \frac{{{K_{ce}}}}{{{A_p}}}\sqrt {\frac{{{\beta _e}{M_t}}}{{{V_t}}}} + \frac{{{B_p}}}{{4{A_p}}}\sqrt {\frac{{{V_t}}}{{{\beta _e}{M_t}}}} {\text{，}}$ (5)

 $\frac{{{q_l}}}{{\Delta i}} = \frac{{{k_{sv}}}}{{\frac{{{s^2}}}{{{\omega _{sv}}}} + 2\frac{{{\zeta _{sv}}}}{{{\omega _{sv}}}}s + 1}}{\text{，}}$ (6)

 $I\left( s \right) = {K_a}{U_s}{\text{，}}$ (7)

 ${U_f}\left( s \right) = {x_p}{\rm{\cdot}}{K_f}{\text{。}}$ (8)

 图 4 控制系统方块图 Fig. 4 Control system block diagram
3 AMEsim建模和仿真

 图 5 液压控制原理图 Fig. 5 Hydraulic control principle diagram

 图 6 不同增益参数的位移输出曲线 Fig. 6 Displacement output curve under different different gain parameters

 图 7 增益参数250的动态误差 Fig. 7 Dynamic error of gain parameter 250

 图 8 增益参数350的动态误差 Fig. 8 Dynamic error of gain parameter 350

 图 9 增益参数450的动态误差 Fig. 9 Dynamic error of gain parameter 450

4 液压系统建模优化

Kp取值范围：100，200，300，400，500，600，700，800；

Kd取值范围：0.01，0.05，0.1，1，5，50，100，200。

 图 10 优化后的液压控制原理图 Fig. 10 After optimization of the hydraulic

 图 11 在PD控制器下的动态误差 Fig. 11 Dynamic error in PD controller control principle diagram

5 结语

1）从图 6~图 9可知，可以通过增加伺服系统增益参数，来减少液压系统的动态位置误差，但随着伺服增益参数的增加，系统输出曲线开始不稳定，发生扰动，因此增加伺服增益参数在一定程度上可以实现提高精度。

2）基于遗传算法进行PD参数的优化选择，可以进行多个参数共同比较，过程简单，同时具有很好的收敛性，避免了盲目筛选参数从而提高工作效率。

3）经过图 10~图 11的仿真研究，说明PD控制算法不但简单而且能够保证系统具有一定的精度及稳定性，能够使一个低稳定性的液压系统变为高稳定性，并且更好地跟随参考模型输出希望数值曲线。

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