电液伺服静动复合加载系统设计及控制优化
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摘要
针对电液伺服静动复合加载机构静载和动载独立加载的特点,简化电液伺服静动复合加载系统为一输入一输出的五阶系统,建立了系统的数学模型。基于经典控制理论确定系统参数,并采用线性二次型最优控制理论以输出值精确跟综理想输入信号为性能指标,对系统的控制方法进行优化,获得最优状态调节器和前馈控制器。通过MATLAB/SIMULINK仿真及试验分别对最优控制理论应用前后系统的闭环频率特性及对正弦波和矩形波的跟踪能力进行对比分析,结果表明,应用最优控制理论的系统频宽更高,对动态信号的跟踪能力更好,控制精度提高明显。
According to the characteristics of static load and dynamic load of electro-hydraulic servo combined loading mechanism, the system is simplified as a five order system of one input and one output, and the linearized mathematical model of the system is established. The system parameters are determined based on the classical control theory. With the output value accurately followed by the expected input signal as the performance index, the linear quadric optimal control theory is used to optimize the control method of the system, and the optimal state regulator and feedforward controller are obtained. The close-loop frequency characteristics of the system before and after the application of the optimal control theory and the tracking ability of the dynamic signals, such as sinusoidal and rectangular waves, are compared and analyzed through MATLAB/SIMULINK and experiment. The results show that the application of optimal control theory has higher system bandwidth, better tracking ability for dynamic signals and higher control accuracy.
According to the characteristics of static load and dynamic load of electro-hydraulic servo combined loading mechanism, the system is simplified as a five order system of one input and one output, and the linearized mathematical model of the system is established. The system parameters are determined based on the classical control theory. With the output value accurately followed by the expected input signal as the performance index, the linear quadric optimal control theory is used to optimize the control method of the system, and the optimal state regulator and feedforward controller are obtained. The close-loop frequency characteristics of the system before and after the application of the optimal control theory and the tracking ability of the dynamic signals, such as sinusoidal and rectangular waves, are compared and analyzed through MATLAB/SIMULINK and experiment. The results show that the application of optimal control theory has higher system bandwidth, better tracking ability for dynamic signals and higher control accuracy.
引文
[1]路德春,李萌,王国盛,等.静动组合载荷下混凝土率效应机理及强度准则[J].力学学报,2017,49(4):940-952.
[2]宫凤强,李夕兵,刘希灵,等.一维动静组合加载下砂岩动力学特性的试验研究[J].岩石力学与工程学报,2010,29(10):2076-2085.
[3]钱七虎.深部岩体工程响应的特征科学现象及“深部”界定[J].东华理工学院学报,2004,27(1):1-5.
[4]邢彤,左强,杨永.帅,等.液压激振技术的研究进展[J].中国机械工程,2012,23(3):362-367.
[5]关景泰,徐宝富.电液伺服振动台静态设计[J].机械设计与研究,1990,6(2):1-4.
[6]高春华,纪金豹,闫维明,等.地震模拟振动台技术在中国的发展[J].土木工程学报,2014(8):9-19.
[7]贾丽花.电液伺服地震模拟振动台控制算法的研究与实现[D].北京:北京交通大学,2007.
[8]李伟荣.高频单轴电液振动台振动特性研究[D].杭州:浙江工业大学,2013.
[9]袁立鹏,崔淑梅,靳蒙.基于迭代学习的液压角振动台控制策略研究[J].宇航学报,2010,31(3):902-906.
[10]余士品,刘续兴,王吉成,等.BP神经网络在电液振动台控制中的应用[J].液压与气动,2008(7):53-55.
[11]刘长年.液压伺服系统的分析与设计[M].北京:科学出版社,1985.
[12]陈利斌,姜伟,裘信国.材料疲劳试验机电液力伺服系统的建模与仿真[J].轻工机械,2010,28(4):73-75.
[13]王幼民,周革,王曙光.基于MATLAB的电液力伺服系统设计与优化[J].机械传动,2005,29(4):46-49.
[14]李鹏,李鑫明,陈安伟,等.直流微电网DC/DC双向换流器时间-状态协调最优控制[J].电网技术,2018,42(1):41-47.
[15]秦大同,隗寒冰,段志辉,等.重度混合动力汽车油耗和排放多目标实时最优控制[J].机械工程学报,2012,48(6):83-89.
[16]方圣楠,宋健,宋海军,等.基于最优控制理论的电动汽车机械式自动变速器换档控制[J].清华大学学报:自然科学版,2016(6):580-586.
[17]邝宏武.机器人捕捉运动目标的时间最优控制方法[J].机械设计与研究,2009,25(6):31-35.
[2]宫凤强,李夕兵,刘希灵,等.一维动静组合加载下砂岩动力学特性的试验研究[J].岩石力学与工程学报,2010,29(10):2076-2085.
[3]钱七虎.深部岩体工程响应的特征科学现象及“深部”界定[J].东华理工学院学报,2004,27(1):1-5.
[4]邢彤,左强,杨永.帅,等.液压激振技术的研究进展[J].中国机械工程,2012,23(3):362-367.
[5]关景泰,徐宝富.电液伺服振动台静态设计[J].机械设计与研究,1990,6(2):1-4.
[6]高春华,纪金豹,闫维明,等.地震模拟振动台技术在中国的发展[J].土木工程学报,2014(8):9-19.
[7]贾丽花.电液伺服地震模拟振动台控制算法的研究与实现[D].北京:北京交通大学,2007.
[8]李伟荣.高频单轴电液振动台振动特性研究[D].杭州:浙江工业大学,2013.
[9]袁立鹏,崔淑梅,靳蒙.基于迭代学习的液压角振动台控制策略研究[J].宇航学报,2010,31(3):902-906.
[10]余士品,刘续兴,王吉成,等.BP神经网络在电液振动台控制中的应用[J].液压与气动,2008(7):53-55.
[11]刘长年.液压伺服系统的分析与设计[M].北京:科学出版社,1985.
[12]陈利斌,姜伟,裘信国.材料疲劳试验机电液力伺服系统的建模与仿真[J].轻工机械,2010,28(4):73-75.
[13]王幼民,周革,王曙光.基于MATLAB的电液力伺服系统设计与优化[J].机械传动,2005,29(4):46-49.
[14]李鹏,李鑫明,陈安伟,等.直流微电网DC/DC双向换流器时间-状态协调最优控制[J].电网技术,2018,42(1):41-47.
[15]秦大同,隗寒冰,段志辉,等.重度混合动力汽车油耗和排放多目标实时最优控制[J].机械工程学报,2012,48(6):83-89.
[16]方圣楠,宋健,宋海军,等.基于最优控制理论的电动汽车机械式自动变速器换档控制[J].清华大学学报:自然科学版,2016(6):580-586.
[17]邝宏武.机器人捕捉运动目标的时间最优控制方法[J].机械设计与研究,2009,25(6):31-35.