Research on anti time-varying disturbance control of wind tunnel flow field
-
摘要: 时变干扰问题在风洞流场控制中普遍存在,其中比较常见和典型的是跨声速连续变迎角试验中迎角对马赫数控制带来的干扰。为了在存在时变干扰情况下提高流场控制精度,创新的提出了一种新型的前馈 + 反馈复合控制方案。前馈控制采用基于超前校正的增量式扩张状态观测器(Lead Correction based Incremental Extend State Observer,LIESO),反馈控制采用增量式比例积分(Proportional-Integral,PI)控制。以1.2 m跨超声速风洞连续变迎角试验为研究对象,对该复合控制方法进行试验验证。试验结果表明:LIESO + PI复合控制对时变干扰抑制效果显著,鲁棒性较好,对不同的模型堵塞度、试验马赫数适应性较好,具有较好的工程应用价值。Abstract: The time-varying disturbance problems are common in wind tunnel flow field control, the most typical of which is the disturbance of Mach number control caused by angle of attack in the transonic continuous sweep angle of attack test. In order to improve the accuracy of flow field control in the presence of time-varying disturbance, a novel feed-forward feedback composite control scheme is innovatively proposed. The feed-forward control is based on phase Lead Correction based Incremental Extend State Observer(LIESO), and the feedback control is based on the incremental Proportional-Integral(PI) control. The research on the transonic continuous sweep angle of attack test in the 1.2 m trans-supersonic wind tunnel is carried out to verify the composite control method. The test results show that: the LIESO + PI composite control method has remarkable effect on time-varying disturbance suppression, and good robustness, good adaptability to different model blockage and test Mach numbers, and has good engineering application value.
-
表 1 试验工况
Table 1. Test condition
试验模型 堵塞度 马赫数 迎角范围/(° ) A 0.98% 0.6 −5~28 A 0.98% 1.05 −5~17 B 0.54% 1.2 −5~21 -
[1] 谢艳, 李平, 蒋鸿, 等. 2.4m跨声速风洞连续变迎角试验关键技术研究[J]. 实验流体力学, 2014, 28(1): 89–93. doi: 10.11729/syltlx20120182XIE Y, LI P, JIANG H, et al. The key technique research on continuous sweeping angle of attack test in 2.4 × 2.4 m transonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2014, 28(1): 89–93. doi: 10.11729/syltlx20120182 [2] 袁平, 易凡, 肖宇航, 等. 面向攻角变化的风洞流场模型预测控制器[J]. 控制与决策, 2018, 33(6): 1026–1032. doi: 10.13195/j.kzyjc.2017.0164YUAN P, YI F, XIAO Y H, et al. Orienting of attack angle based model prediction controller of wind tunnel flow[J]. Control and Decision, 2018, 33(6): 1026–1032. doi: 10.13195/j.kzyjc.2017.0164 [3] ZHANG J, YUAN P, CHIN K S. Model predictive control for the flow field in an intermittent transonic wind tunnel[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 324–338. doi: 10.1109/TAES.2017.2756538 [4] 易凡, 李欣蕊, 杜宁, 等. 基于迭代学习的风洞马赫数控制方法[J]. 控制工程, 2020, 27(1): 109–113. doi: 10.14107/j.cnki.kzgc.20190333YI F, LI X R, DU N, et al. Iterative learning based control for wind tunnel Mach number[J]. Control Engineering of China, 2020, 27(1): 109–113. doi: 10.14107/j.cnki.kzgc.20190333 [5] SOETERBOEK R A M, PELS A F, VERBRUGGEN H B, et al. A predictive controller for the Mach number in a transonic wind tunnel[J]. IEEE Control Systems Magazine, 1991, 11(1): 63–72. doi: 10.1109/37.103359 [6] NGUYEN N, ARDEMA M. Predictive optimal control of a hyperbolic distributed model for a wind tunnel[J]. Journal of Guidance, Control, and Dynamics, 2006, 29(3): 626–634. doi: 10.2514/1.15381 [7] SUTCLIFFE P, RENNIE M R. Neural network model predictive control of wind tunnel test conditions[C]//Proceedings of the 54th AIAA Aerospace Sciences Meeting. 2016. doi: 10.2514/6.2016-1150 [8] 吕伍, 毛志忠, 袁平, 等. 基于模型迁移方法的精炼炉钢水终点硫含量预报[J]. 东北大学学报(自然科学版), 2014, 35(3): 314–317. doi: 10.3969/j.issn.1005-3026.2014.03.003LYU W, MAO Z Z, YUAN P, et al. Ladle furnace end point sulphur content prediction model based on model migration method[J]. Journal of Northeastern University (Natural Science), 2014, 35(3): 314–317. doi: 10.3969/j.issn.1005-3026.2014.03.003 [9] 袁平, 王福利, 毛志忠. 基于案例推理的电弧炉终点预报[J]. 东北大学学报(自然科学版), 2011, 32(12): 1673–1676. doi: 10.12068/j.issn.1005-3026.2011.12.001YUAN P, WANG F L, MAO Z Z. CBR based endpoint prediction of EAF[J]. Journal of Northeastern University (Natural Science), 2011, 32(12): 1673–1676. doi: 10.12068/j.issn.1005-3026.2011.12.001 [10] 刘为杰, 何帆, 凌忠伟. 2.4m跨声速风洞流场预测自抗扰控制[J]. 航空学报, 2019, 40(11): 123154. doi: 10.7527/DS1000-6893.2019.23154LIU W J, HE F, LING Z W. Predictive active disturbance rejection control for flow field in 2.4 m transonic wind tunnel[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(11): 123154. doi: 10.7527/DS1000-6893.2019.23154 [11] 周波, 高川, 杨洋. 2m超声速风洞流场变速压控制方法研究[J]. 实验流体力学, 2019, 33(6): 72–77. doi: 10.11729/syltlx20180133ZHOU B, GAO C, YANG Y. Study on varying dynamic pressure control of flow field in 2m supersonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(6): 72–77. doi: 10.11729/syltlx20180133 [12] 芮伟, 易凡, 杜宁, 等. 2.4m跨声速风洞颤振试验流场控制技术研究[J]. 实验流体力学, 2012, 26(6): 83–86. doi: 10.3969/j.issn.1672-9897.2012.06.018RUI W, YI F, DU N, et al. Study on flow field control technique of flutter test in 2.4m transonic wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2012, 26(6): 83–86. doi: 10.3969/j.issn.1672-9897.2012.06.018 [13] 韩京清. 自抗扰控制技术[J]. 前沿科学, 2007, 1(1): 24–31. doi: 10.3969/j.issn.1673-8128.2007.01.004HAN J Q. Auto disturbances rejection control technique[J]. Frontier Science, 2007, 1(1): 24–31. doi: 10.3969/j.issn.1673-8128.2007.01.004 [14] HAN J Q. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics, 2009, 56(3): 900–906. doi: 10.1109/TIE.2008.2011621 [15] GAO Z Q. Scaling and bandwidth-parameterization based controller tuning[C]//Proc of the Proceedings of the 2003 American Control Conference. 2003. doi: 10.1109/ACC.2003.1242516 [16] ZHAO S, GAO Z Q. Active disturbance rejection control for non-minimum phase systems[C]//Proceedings of the 29th Chinese Control Conference. 2010. -