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作 者:张晓宇[1] 王润孝[1] 王战玺[1] 张顺琦[2] 秦现生[1] 谭小群[1] Zhang Xiaoyu Wang Runxiao Wang Zhanxi Zhang Shunqi Qin Xiansheng Xiaoqun(School of Mechanilcal Engineering, Nothwestem Polytechnical University, Xi'an 710072, China School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)
机构地区:[1]西北工业大学机电学院,陕西西安710072 [2]上海大学机电工程与自动化学院,上海200072
出 处:《西北工业大学学报》2017年第4期661-668,共8页Journal of Northwestern Polytechnical University
基 金:国家自然科学基金(51505380;51475373;51375390);机械结构力学及控制国家重点实验室开放课题(MCMS-0517G01);111引智计划(B13044)资助
摘 要:针对受未知扰动和建模误差影响的压电柔性机械臂系统,提出并开发了一种H_∞优化的抗扰控制算法并将其应用于压电柔性机械臂的振动抑制。运用有限元法建立了压电柔性机械臂的机电耦合动力学模型及状态空间模型。使用由阶跃函数构成的扰动模型,构造了比例积分(PI:proportional integral)扰动观测器。基于H_∞优化方法,并应用S/T混合灵敏度和区域极点配置分别计算了扰动观测器和抗扰控制器的反馈增益,保证了观测器与控制器的动态性能和鲁棒性。通过压电柔性机械臂振动抑制仿真,验证了H_∞优化抗扰控制算法抑制未知扰动所致振动的有效性和对系统模型误差的鲁棒性,并与使用通用比例积分(GPI:generalized proportional integral)观测器的抗扰控制算法进行了比较。ling errors, For piezoelectric integrated flexible manipulator systems influenced by unknown disturbances and model- a H~ optimized disturbance rejection control is developed and applied to vibration suppression of piezo- electric integrated flexible manipulators in this paper. Firstly, tile electro-mechanically coupled dynamic model and state space model of the flexible manipulator are built using finite element method. Secondly a Proportional Integral (PI) disturbance observer is constructed using a disturbance model composed of step functions. Subsequently, based on the H∞ optimization theory, the feedback gains of the observer and controller are calculated employing S/T mixed sensitivity and regional pole placement method respectively, by which the dynamic performance and robustness of the closed-loop systems are ensured. Lastly, through vibration suppression simulations of piezoelectric inte- grated flexible manipulators, the H∞ optimized disturbance rejection control is validated and compared with disturb- ance rejection control with Generalized Proportional Integral (GPI) observer, in the aspects of suppressing vibra- tions caused by unknown disturbances and robustness to modelling errors.
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