提出了一种新的有约束的复杂随动系统——小碗摆球系统,并针对该系统利用达朗伯静力学的方法进行了建模。在讨论了系统能控和能观性的基础上分别采用状态反馈的极点配置法和基于遗传算法的LQR最优控制2种方法进行了实际系统控制效果的实验对比,通过对比可知极点配置的状态反馈控制器具有更好的鲁棒性和瞬态特性,而遗传算法优化的LQR控制具有更好的稳态特性,以及更短的调节时间。同时在参数选择方面相比于极点配置试特征值的方法,遗传算法优化LQR控制控制器更有针对性,便于实际的应用操作。
Abstract
A new constrained complex follow-up system-cup and ball pendulum system was proposed. The new system was modeled by the method of lagrange dynamics. controllability and observability of the system were studied firstly. Two control methods of state feedback controller with pole assignment and LQR optimal control based on genetic algorithm were used to achieve the experiment comparison in this paper. Through the comparative analysis, state feedback controller with pole assignment has better robustness and transient characteristics. And LQR optimal control based on genetic algorithm has better steady-state characteristics and shorter tuning time. Meanwhile, compared with state feedback controller with pole assignment LQR optimal control based on genetic algorithm is more targeted in terms of parameter selection, therefore it is convenient for practical application.
关键词
计量学;随动系统;小碗摆球系统 /
极点配置;遗传算法;LQR最优控制
Key words
metrology /
follow-up system /
cup and ball pendulum system /
pole assignment /
genetic algorithms /
LQR optimal control
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参考文献
［1］钱富才, 刁俊, 杨恒占, 等. 复杂系统多模型学习与控制优化设计方法［J］. 系统工程理论与实践, 2016, 36(1): 200-208.
Qian F C, Diao J, Yang Z H, et al. Multiple model learn and optimization design method for complex systems ［J］. Systems Engineering Theory & Practice, 2016, 36(1): 200-208.
［2］Hasson C J, Tian S, Sternad D. Energy margins in dynamic object manipulation ［J］. Journal of Neurophysiol, 2012, 108(5):1349-1365.
［3］Zarafshan P, Moosavian S A A. Cooperative Object Manipulation by a Space Robot with Flexible Appendages ［J］. ISRN Aerospace Engineering, 2013, ID 965481.
［4］丁峰. 系统辨识算法的复杂性、收敛性及计算效率研究［J］. 控制与决策, 2016, 31(10): 1729-1741.
Ding F. Complexity, convergence and computational efficiency for system identification algorithms ［J］. Control and Decision, 2016, 31(10): 1729-1741.
［5］时培明, 孙鹏, 袁丹真. 基于非线性耦合双稳态随机共振的轴承微弱故障信号增强检测方法研究［J］.计量学报, 2018, 39(3): 373-377.
Shi P M, Sun P, Yuan D Z. Research on the Enhanced Detection Method of Bearing Fault Weak Fault Signal Based on Nonlinear Coupled Bistable Stochastic Resonance ［J］. Acta Metrologica Sinica, 2018, 39(3): 373-377.
［6］胡红波, 孙桥. 基于状态空间模型的IIR滤波运算不确定度评估［J］. 计量学报, 2017, 38(3): 351-355.
Hu H B, Sun Q. Uncertainty Evaluation of Filtering Based on State-space Model ［J］. Acta Metrologica Sinica, 2017, 38(3): 351-355.
［7］张晓红, 曾建潮. 基于状态空间分割的两部件系统机会维修优化［J］. 系统工程理论与实践, 2015, 35(6): 1547-1560.
Zhang X H, Zeng J C. State space partition based optimization of the opportunistic preventive maintenance of two-unit systems ［J］. Systen Engineering-Theory & Practice, 2015, 35(6): 1547-1560.
［8］宋娜, 石玉, 周克印. 遗传算法在EMD虚假分量识别中的应用［J］. 计量学报, 2015, 36(4): 413-417.
Song N, Shi Y, Zhou K Y. Applying Genetic Algorithms in EMD False Component Identification ［J］. Acta Metrologica Sinica, 2015, 36(4): 413-417.
［9］严建华, 黄群星, 马增益, 等. 遗传算法用于颗粒粒度分布重建的研究［J］. 计量学报, 2005, 26(1): 86-89.
JH Yan J H, Huang Q X, Ma Z Y, et al. Study on Particles Size Distribution Recovering by Evolutionary Program ［J］. Acta Metrologica Sinica, 2005, 26(1): 86-89.
［10］滕峰成,张昊阳,程安迪,等. 基于非线性遗传算法的DTS传感模型的参数辨识与优化［J］. 计量学报, 2019, 40(4): 610-617.
Teng F C, Zhang H Y, Cheng A D, et al. Parameter Identification and Optimization of the DTS Sensing Model Based on the Nonlinear Genetic Algorithm［J］. Acta Metrologica Sinica, 2019, 40(4): 610-617.
基金
国家自然科学基金(51605419);中国博士后基金(2016M600193);河北省自然科学基金(F2018203433);河北省引进留学人员资助(CL201727)
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