Design of hydraulic motor speed control system based on
co-simulation of AMESim and Matlab Simulink
MENG Fan-hu, ZHAO Su-su, LEI Xiao-shun, WANG Na, GAO Feng
{Laboratory of Road Construction Technology and Equipment, Chang1 an University, Xian 710064 , China)
Abstract: In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation (PID) control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved
Key words: speed control system; co-simulation; neural network; proportion-integration-differentiation (PID) control
CLD number: TH137. 51
Document code: A
Article ID: 1674-8042(2016)03-0279-07
doi: 10. 3969/j. issn. 1674-8042. 2016. 03. 012
A hydraulic drive device is a kind of common transmission device. Compared with other types of transmission devices, it has advantages of high power density, sensitive action and easy realization of stepless speed change control^. A hydraulic motor is a common actuator in the hydraulic drive system . There are valve controlled motor and pump controlled motor hydraulic systems using motors as actuators1-2-1.
The valve control motor system has fast response frequency, short regulating time, good dynamic characteristic and high efficiency, therefore it is suitable for the system with small power but fast response speed. Motor speed control is the core of the whole system that has requirements for varied speed. Valve controlled motor system is a complex nonlinear timevarying system. Due to the characteristics of hydraulic oil, temperature, leakage and other factors, it is very difficult to establish a precise mathematical model。]. Therefore we need to simplify the system to establish a mathematical model in the Matlab simulation. In order to make the mathematical model more accurateraquo; AMESim is adopted. As a result, the hydraulic model is established in AMESim and control part is designed in Matlab_Simiulnk based on cosimulation of AMESim and Simulink. The simulation results are closer to the actual prototype by making full use of the advantages of two kinds of simulation software'*〕.
In the valve control motor controlled system, the proportion-integration-differentiation (PID) controller is usually used to realize the constant speed control of the motor speed. PID controller has the advantage of simplicity, relatively good adaptability and strong robustness, thus it is widely used in the automatic system. But PID controller can only be used in the system with precise mathematical model, For the application system with nonlinearity and time-varying uncertainty, it is difficult to describe the accurate mathematically model. The reasons that the effect of traditional PID controller is not ideal are as follows. On the one hand, the difficulty of determining parameters of traditional PID controller makes performance poor, on the other hand, we need to regulate PID parameters when the system changes. A variety
Received date: 2016-06-03
Corresponding author: MENG Fan-hu (xixuanmenglan@ 163. com)
of improved PID controllers have appeared with the development of control technology ? such as fuzzy PID, expert PID, PID parameter tuning based on genetic algorithm, grey PID, neural network PICP〕. In this paper, PID controller designed based on single neuron control theory is aimed at improving the speed performance of valve controlled motor system using co-simulation of AMESim and Mablab_Simulink.
- Introduction of valve controlled motor control system
- 1 Control principle of valve controlled hydraulic motor
In this paper, three-position four-way electro-hydraulic proportional valve is used in the valve controlled motor system, and the system structure is shown in Fig. 1.
Motor speed instructions are given by the voltage UT, and the actual speed of the motor is converted into voltage signal via the speed sensor and then is input to a comparator by means of negative feedback. After calculating the deviation Ue by comparing input voltage signal with feedback signal, the deviation signal is enlarged as the input signal of electro-hydraulic proportional valve. The electrical signals control the valve opening size, correspondingly control the flow and speed of the hydraulic motor. The output shaft of the motor is connected with the pump shaft through the coupling, and the motor can be loaded by a simulation loading system.
Fig. 1 Structure diagram of electro proportional valve controlled motor speed control system
1. 2 Establishment of AMESim simulation model and parameter settings
According to the selected components in the experiment ,the model of the system is established in AMESim and the simulation parameters of the components and the system are set. The model is shown in Fig. 2.
Fig. 2 Model of valve controlled motor syston in AMESim
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CSC和DSC铁水脱硫研究进展
LIN Yan-chian, CHENG Hsien-hua, WANG Muh-shun, HUANG Jun-lin
引言:CSC铁水脱硫现有两种工艺。一种是鱼雷车内的喷射过程,另一种是转运罐内的推进过程。石灰基焊剂已用于这两种工艺。前者于1982年引入CSC。后者分别于2004年和2010年被CSC和DSC采用。本文对石灰基脱硫剂进行了理论分析,并对Kambara(或Kikai)反应器的水模型和热模型进行了实验评价。此外,还比较了炼钢车间两种工艺的近期大规模生产性能。
关键词:铁水脱硫;喷射工艺;推进工艺
除易切削钢外,大多数钢种的硫含量要求低于60ppm,以获得更好的内部和表面质量。高炉铁水脱硫是综合炼钢厂获得低硫钢的关键工序。表1显示了不同工艺使用的各种处理容器、熔剂和熔剂添加类型。
1982年,CSC引进了在鱼雷车内注入石灰基焊剂的工艺,如图1所示。在2001年对石灰基熔剂顶装水模和热模推进工艺进行了试验评价后,2004年在CSC公司实施了两套转罐KR(Kambara或Kikai反应器)工艺。2010年DSC采用了另外两套KR工艺。2012年,DSC还将增加两套KR工艺。
图1 鱼雷车喷粉工艺示意图
1铁水脱硫石灰基熔剂的理论分析
铁水与石灰的脱硫反应可用式(1)表示。式(1)的标准自由能()和平衡常数(Ke)可分别用式(2)和式(3)来描述。
(1)
=27050-27.55T(K) (2)
Ke=(aCaS.PCO)/(aCaO.aS.aC) (3)
式(3)中和的活性可作为一个整体,因为它们是纯固体CaS和CaO。由于铁水中石墨的饱和作用,活性炭的活性也可以统一。CO(PCO)的分压可以假定为1atm。硫在稀溶液中的活度系数(fS)约为5。平衡状态下11ppm的硫含量可由式(2)、式(3)、式(4)和式(5)在1300℃下的组合计算。结果表明,用廉价的石灰基熔剂脱硫后铁水硫含量可达到要求的低水平
log(aCaS.PCO)/(aCaO.aS.aC)=2.26 (4)
[%S]=aS/fS=0.0011 (5)
此外,式(1)的脱硫率可用式(6)表示。Ls是炉渣硫含量与铁水硫含量的分配比。脱硫速率常数(ks)可以是搅拌能量(E)和炉渣成分(CSLAG)的函数,如式(7)所示。转包内搅拌过程的搅拌能量通常大于鱼雷车内喷射过程的搅拌能量,并在较短时间内趋于热力学平衡状态。因此,只要控制好炉渣成分,采用搅拌法可获得较好的脱硫效果。
-d[S%]/dt = ks([S%]-(S%)/Ls) (6)
ks =f(E,CSLAG) (7)
2 KR推进过程的实验评价
采用有机玻璃水包叶轮水模型对KR工艺进行了模拟研究。如图2所示,发现叶轮周围有涡流。涡流深度随着叶轮转速的增加而增加,如图3所示。
图2 叶轮周围的涡流
图3 涡流深度与叶轮转速的关系
然后,以锰酸钾(KMnO4)饱和溶液为显色剂,测定均匀混合时间。搅拌时间与叶轮转速呈负指数关系,如图4所示。
图4 搅拌时间与叶轮转速的关系
当叶轮转速大于100rpm时,搅拌时间可小于2min。另外,将塑料颗粒充入水模型中,模拟了石灰基熔剂脱硫渣的固态。当叶轮转速大于100rpm时,如图5所示,红色塑料颗粒深入水中,意味着炉渣与铁水的良好混合效果。
图5 塑料颗粒的混合效应
同时,在CSC实验室建立了KR过程的热模型,如图6所示。冷生铁在250kg铁水包感应熔炼炉中熔炼。四叶片叶轮表面涂有耐火浇注料。图7显示了热态模型试验的结果,并揭示了采用较高的叶轮转速、较小的助熔剂粒径和较大的助熔剂量,可在15分钟内将铁水中的硫含量降低到40ppm以下。
图6 KR过程的热模型
图7 热模型脱硫结果
3注射和推进过程的量产性能
脱硫度和炉渣碱度分别定义为式(8)和式(9)。式中,式(8)中的[S]o和[S]f为铁水处理前后的硫含量。图8描述了2004年KR工艺热试车24炉De-S%与B2的关系。
图8 脱硫度与炉渣碱度
化学分析用脱硫渣在拉渣过程中取样,如图9所示。
图9 KR工艺拖渣过程中的渣样采集
较高的De-S%伴有较高的B2。表2列出了图8所示的4次异常加热的数据。B2小于3.0可能是导致脱硫率小于40%的原因。
De-S(%)=([S]o-[S]f)times;100/[S]o (8)
B2=(CaOwt.%)/(SiO2wt%) (9)
2(CaO) 2[S] [Si]=2(CaS) (SiO2) (10)
(SiO2) 2(CaO)=(2CaO·SiO2) (11)
如式(11)所示,式(10)中的二氧化硅产物容易形成硅酸二钙,但铁水中的硅含量可以提高脱硫效果。硅酸二钙的形成不仅会排出氧化钙,而且会覆盖石灰颗粒表面,阻碍脱硫反应的进行。因此,铁水脱硫需要足够的B2。此外,通过熔融试验测得的脱硫渣液相线和液相温度如图10所示,说明渣是固态的。
表2 KR热试车中图8中4炉异常处理前后铁水的渣成分及硫含量
图11、图12、图13为2010年全年CSC鱼雷车喷粉工艺和CSC、DSC转包KR推进工艺在金属液硫含量控制上的量产情况。铁水脱硫度和熔剂单耗的平均数据如图11所示。虽然喷射法脱硫率可达80%以上,但所需时间较长。
图10 熔渣熔化试验
图11 2010年脱硫性能
综合炼钢车间金属液硫含量的稳定控制具有重要意义。这意味着不仅需要对铁水进行有效的脱硫,而且还需要减少后续转炉过程中的再气化。转炉出钢后从铁水到钢液的平均硫含量历史如图12所示。鱼雷车内的喷射过程在二次精炼前,处理过的铁水和钢液之间的再气化程度最高,如图13所示。2010年CSC第二转炉炼钢车间采用转包KR推进工艺对鱼雷车喷吹工艺的替代率提高到60%以上。
图12 金属镀液中硫含量的历史
图13 2010年金属浴的再气化
4结论
1) 从理论上分析,石灰基熔剂脱硫后铁水的硫含量,只要搅拌能量强,渣成分好,就可以达到要求的低水平。
2) 通过水模型和热模型试验评价,采用石灰基助熔剂的KR推进工艺可以有效地脱硫铁水。
3) 通过对KR炉渣成分的分析,发现炉渣碱度越高,脱硫效果越好。
4) 从量产性能来看,转罐KR搅拌工艺的熔剂单耗和铁水到钢液的再气化均低于鱼雷车喷吹工艺。
引用文献
[1] Robert D. Pehlke, Unit Processes of Extractive Metallurgy [M], 5th ed. New York, Elsevier North Holland, 1982
[2] William G. Wilson and Alex McLean, Desulfurization of Iron and Steel and Sulfide Shape Control [M], Warrendale PA.,The Iron and Steel Society of AIME, 1980
[3] ASTM D 1857-87, Standard Test Method for Fusibility of Coal and Coke Ash, 1972.
基于AMESim和Matlab_Simiulink联合仿真的
液压马达转速控制系统设计
孟凡虎,赵素素,雷晓顺,王娜,高峰
(长安大学道路施工技术与装备教育部重点实验室,陕西西安710064)
引言: 为设计有效的阀控液压马达转速控制系统,采用AMESim和Matlab.Simiulink联合仿真技术来 建立准确的模型,以更好地反应实际系统。将神经元控制应用于PID参数在线调整中,提高了系统的准确 性、快速性和稳定性。仿真结果表明,所设计的系统偏差减小,准确性提高;对于阶跃信号、正弦信号的响 应速度加快,快速性提高;在系统施加干扰的情况下,能够恢复到控制值,稳定性提高。
关键词: 转速控制系统; 联合仿真; 神经网络; (PID) 控制
引用格式: MENG Fan-hu, ZHAO Su-su, LEI Xiao-shun, et al Design of hydraulic motor speed control system based on co-simulation of AMESim and Matlab__Simulink Journal of Measurement Science and Instrumentation, 2016, 7(3): 279-285. [doi: 10. 3969/j. issn 1674-8042. 2016. 03. 012]
液压传动装置是一种常见的传动装置。与其它类型的传动装置相比,它具有功率密度高、动作灵敏、易于实现无级变速控制等优点。液压马达是液压驱动系统中的一种复合执行器。有阀控马达和泵控马达液压系统,使用马达作为执行器。
阀控马达系统具有响应频率快、调节时间短、动态特性好、效率高等特点,适用于功率小、响应速度快的系统。电机调速是整个系统的核心,对调速有着要求。阀控马达系统是一个复杂的非线性时变系统。由于液压油的特性、温度、泄漏等因素的影响,很难建立精确的数学模型。因此,我们需要在Matlab仿真中简化系统建立数学模型。使用AMESim软件使数学模型更精确。AMESim在AMESim和Simulink联合仿真的基础上,在AMESim中建立了液压模型,并在Matlab中设计了控制部分,充分利用两种仿真软件的优点,使仿真结果更接近实际样机。
在阀控马达控制系统中,比例积分微分(PID)控制通常用于实现电机转速的恒速控制。PID控制器具有简单、适应性强、鲁棒性强等优点,在自动控制系统中得到了广泛的应用。但PID控制器只能应用于数学模型精确的系统中,对于具有非线性和时变不确定性的应用系统,精确的数学模型难以描述。传统PID控制效果不理想的原因如下。一方面,传统PID控制器参数难以确定,性能较差;另一方面,当系统发生变化时,需要调整PID参数.
随着控制技术的发展,出现了各种改进的PID控制器?如模糊PID、专家PID、基于遗传算法的PID参数整定、灰色PID、神经网络PICP等。本文利用AMESim和Mablab_Simulink的联合仿真,设计了基于单神经元控制理论的PID控制器,以提高阀控马达系统的调速性能。
1阀控马达控制系统介绍
本文将三位四通电液比例阀应用于阀控马达系统中,系统结构如图1所示.电机速度指令由电压UT给出,电机的实际速度通过速度传感器转换成电压信号,然后通过负反馈输入比较器。通过比较输入电压信号和反馈信号计算偏差Ue后,将偏差信号放大为电液比例阀的输入信号。电信号控制阀门开度大小,相应地控制液压马达的流量和速度。电机的输出轴通过联轴器与泵轴连接,电机可通过模拟加载系统加载。
图1 电比例阀控制电机调速系统结构图
- 1 AMESim仿真模型的建立及参数设置
根据实验中选择的元件,在AMESim系统中建立模型并设定原件和系统的参数,如图2
图2 AMESim中的阀控马达系统模型
系统中主要部件的参数如表1所示。三位四通电液比例阀的输入信号为电机转速,K为电机转速转化为电流的系数。
K的计算公式可用表1中的数据表示, K=40/l168.
lt;
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