基于鸟群算法优化的桥式吊车线性自抗扰控制
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摘要
针对欠驱动吊车的防摆与定位问题,本文提出一种基于线性自抗扰控制技术(LADRC)的欠驱动桥式吊车控制策略及控制器参数优化方法。区别于其他常规的桥式吊车控制策略,该方法不需要对吊车模型进行任何近似解耦或线性化处理,允许模型存在一定的不确定性并且考虑了系统所受的摩擦力与空气阻力等干扰,同时针对传感器噪声污染环境下控制器的参数整定问题,应用鸟群优化算法进行参数优化。在负载质量和吊绳绳长等发生变化或存在不确定因素的情况下,控制策略依然能实现对台车的精确定位与负载摆动的有效抑制。最后通过仿真证明了控制方法具有良好的控制性能。
Aiming at the problem of anti-swing and positioning of underactuated crane, this paper proposes a control strategy of underactuated bridge crane based on linear active disturbance rejection control(LADRC). Different from other conventional bridge crane control strategies, this method does not require any approximate decoupling or linearization of the crane model, allowing the model to have certain uncertainty and considering the friction and air resistance of the system. At the same time, considering the parameter tuning problem of the controller in the environment of sensor noise pollution, the bird swarm optimization algorithm is applied to optimize the parameters. Then the simulation proves that the control method has good control performance.
Aiming at the problem of anti-swing and positioning of underactuated crane, this paper proposes a control strategy of underactuated bridge crane based on linear active disturbance rejection control(LADRC). Different from other conventional bridge crane control strategies, this method does not require any approximate decoupling or linearization of the crane model, allowing the model to have certain uncertainty and considering the friction and air resistance of the system. At the same time, considering the parameter tuning problem of the controller in the environment of sensor noise pollution, the bird swarm optimization algorithm is applied to optimize the parameters. Then the simulation proves that the control method has good control performance.
引文
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[8]Meng X B,Gao X Z,Lu L H,et al.A new bio-inspired optimization algorithm:bird swarm algorithm[J].Journal of Experimental&Theoretical Artificial Intelligence,2016,28(4):673-687
[9]史永丽,侯朝桢,苏海滨.基于粒子群优化算法的自抗扰控制器设计[J].系统仿真学报,2008,20(2):433-436
[10]康忠健,王清伟,黄磊,等.基于改进遗传算法的自抗扰控制器参数优化[J].信息与控制,2008,37(5):588-592
[11]楚东来.基于改进粒子群算法的自抗扰控制器参数优化及仿真[J].信息通信,2015(9):15-17
[12]徐致远.桥式吊车水平运动过程智能防摆控制技术研究[D].沈阳:沈阳工业大学电气工程学院,2005.49-52
[13]蒋理,陈树广.基于模糊控制的桥式起重机定位防摆研究[J].计算机仿真,2009,26(6):179-182
[2]朱长建,姜洪喆,王伟,等.汽车起重机吊物防摆可控因素实验分析及理论研究[J].高技术通讯,2016,26(8):767-772
[3]王志胜,周超,孙俊珍.桥式吊车系统信息融合跟踪控制器设计[J].系统科学与数学,2017,37(3):641-651
[4]刘熔洁,李世华.桥式吊车系统的伪谱最优控制设计[J].控制理论与应用,2013,30(8):981-989
[5]Wu X Q,He X X.Partial feedback linearization control for 3-D underactuated overhead crane systems[J].ISATransactions,2016,65:361-370
[6]韩京清.自抗扰控制技术---估计补偿不确定因素的控制技术[M].北京:国防工业出版社,2016.255-261
[7]Gao Z Q.Scaling and parameterization based controller tuning[C].In:Proceedings of the 2003 American Control Conference,Denver,USA,2003.4989-4996
[8]Meng X B,Gao X Z,Lu L H,et al.A new bio-inspired optimization algorithm:bird swarm algorithm[J].Journal of Experimental&Theoretical Artificial Intelligence,2016,28(4):673-687
[9]史永丽,侯朝桢,苏海滨.基于粒子群优化算法的自抗扰控制器设计[J].系统仿真学报,2008,20(2):433-436
[10]康忠健,王清伟,黄磊,等.基于改进遗传算法的自抗扰控制器参数优化[J].信息与控制,2008,37(5):588-592
[11]楚东来.基于改进粒子群算法的自抗扰控制器参数优化及仿真[J].信息通信,2015(9):15-17
[12]徐致远.桥式吊车水平运动过程智能防摆控制技术研究[D].沈阳:沈阳工业大学电气工程学院,2005.49-52
[13]蒋理,陈树广.基于模糊控制的桥式起重机定位防摆研究[J].计算机仿真,2009,26(6):179-182