航空发动机控制系统执行机构参数在线估计方法

季春生; 王元; 卢俊杰

doi:10.13224/j.cnki.jasp.20220574

航空发动机控制系统执行机构参数在线估计方法

doi: 10.13224/j.cnki.jasp.20220574

季春生^{1, 2,},
王元²,
卢俊杰²

1.
西北工业大学动力与能源学院，西安 710072
2.
中国航发控制系统研究所，江苏无锡 214063

详细信息

作者简介:
季春生（1977-），男，研究员，博士，主要从事航空发动机全权限数字电子控制研究

中图分类号: V233.7
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- 文章访问数: 41
- HTML浏览量: 26
- PDF量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-06
- 网络出版日期: 2023-11-16

Online actuator parameter estimation method for aero-engine control system

1.
School of Power and Energy，Northwestern Polytechnical University，Xi’an 710072，China
2.
Aero Engine Control System Institute，Aero Engine Corporation of China，Wuxi Jiangsu 214063，China

摘要

摘要:
针对航空发动机控制系统因为执行机构性能退化而导致发动机控制品质降低或者严重时威胁发动机运行安全的状况，开展执行机构状态参数在线估计方法研究。在航空发动机实际执行机构控制回路实测信号较少的情况下，提出一种组合参数在线估计方法，通过作动模式识别分类，基于无迹卡尔曼滤波以稳态模式输出估计电液伺服阀平衡电流，基于拟牛顿算法（BFGS）以动态模式输出估计执行机构增益和作动延迟时间，实现模型参数的在线更新，建立实时自适应执行机构模型。以某涡扇发动机导叶作动控制回路为对象进行仿真，结果表明：在只有作动位置单一参数可测的条件下，在不同作动状态下对执行机构控制回路的平衡电流估计误差优于±0.2 mA，执行机构增益估计误差优于±4%，作动延迟周期估计误差不超过1个控制周期，能够实时跟踪并较为准确地估计执行机构的工作状态，为航空发动机执行机构控制回路设计与故障诊断提供技术支撑。
- 航空发动机控制系统 /
- 执行机构 /
- 自适应模型 /
- 参数估计 /
- 模式识别分类 /
- 状态跟踪
Abstract:
In order to solve the problem of performance degradation that consequently reduces the engine control quality and even endangers the engine operation safety, a method of estimating the real-time state and performance variation trend for the actuator was proposed. Considering the measurable signals of practical aero-engine servo actuator were less than the performance parameters, a adaptive estimation method of combined state was put forward by means of recognition and classification of the actuation pattern, the balanced current of electro-hydraulic servo in the steady state was estimated by unscented Kalman filter, the actuation gain and actuation time delay in the dynamic state were estimated by the method of Broyden-Fletcher-Goldforb-Shanno (BFGS) , the performance parameters were updated in real time, and the adaptive model of servo actuator was established. A vane servo actuator loop of turbofan engine was simulated. The simulation results showed that, when single servo parameter can be measured, the absolute error of balance current estimation was less than ±0.2 mA, the relative error of actuation gain estimation was less than 4%, and the absolute error of actuation delay period estimation was less than one control period in different actuation states, and the adaptive model can estimate the state of actuator accurately and track the performance variation trend in real time, so the method can provide technical support of the control loop design and fault diagnosis for aero-engine servo actuator.
- aero-engine control system /
- actuator /
- adaptive model /
- parameters estimation /
- pattern recognition and classification /
- state tracking

HTML

图 1 导叶执行机构控制回路结构图

Figure 1. Structure of guide vane servo-control system

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图 2 电液伺服阀和作动筒工作原理

Figure 2. Operational mechanism of electro-hydraulic servo valve and actuator cylinder

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图 3 组合状态自适应估计算法结构图

Figure 3. Structure diagram of adaptive estimation algorithm for combined states

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图 4 基于UKF的稳态参数估计原理图

Figure 4. Steady-state parameter estimation schematic diagram based on UKF

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图 5 BFGS在线优化原理图

Figure 5. BFGS online optimization schematic diagram

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图 6 枚举型优化器组

Figure 6. Enumeration type optimizer group

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图 7 作动控制系统闭环控制仿真回路

Figure 7. Closed-loop control loop of servo-control system

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图 8 阶跃作动时的平衡电流漂移估计

Figure 8. Estimation of balance current drift during conventional servo actuation

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图 9 阶跃作动时的作动增益退化估计

Figure 9. Estimation of actuation gain degradation during conventional servo actuation

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图 10 阶跃作动时的作动延迟退化估计

Figure 10. Estimation of actuation delay degradation during conventional servo actuation

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图 11 异常波动时的作动增益退化估计

Figure 11. Estimation of actuation gain degradation during abnormal servo fluctuation

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图 12 异常波动时的作动增益和作动延迟退化估计

Figure 12. Estimation of actuation gain and actuation delay degradation during abnormal servo fluctuation

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图 13 异常波动时的平衡电流、作动增益和作动延迟退化估计

Figure 13. Estimation of balance current and actuation gain and actuation delay degradation during abnormal servo fluctuation

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图 14 半物理试验正行程阶跃模型辨识效果

Figure 14. Model identification in forward stroke step of semi-physical experiment

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图 15 半物理试验反行程阶跃模型辨识效果

Figure 15. Model identification in reverse stroke step of semi-physical experiment

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表 1 阶跃响应下参数在线估计效果

Table 1. Online parameters estimation effect in the step response process

模拟特征参数	试验序号	作动增益/（%/（s·mA））		平衡电流/mA		作动延迟周期
模拟特征参数	试验序号	估计值	相对误差/%	估计值	绝对误差	估计值	绝对误差
$ \begin{gathered}I_{\text{bal}}=5\; \mathrm{mA} \\ K_{\rm{\mathrm{v}}}=5\text{%}/ （\mathrm{s}\cdot\mathrm{mA}） \\ t_{\text{delay}}=5 \\ \end{gathered} $	1	4.83	−3.4	4.99	−0.01	5	0
	2	5.03	0.6	5.00	0	5	0
	3	5.03	0.6	4.99	−0.01	5	0
	4	5.01	0.2	5.01	0.01	5	0
	5	5.00	0	5.00	0	5	0
$ \begin{gathered} {I_{{\text{bal}}}} = 7.5 \;{\mathrm{mA}} \\ {K_{\rm{v}} }= 7.5 {\text{%}}/ （{\mathrm{s}}\cdot {\mathrm{mA}}） \\ {t_{{\text{delay}}}} = 10 \\ \end{gathered} $	6	7.42	−1.0	7.49	−0.01	10	0
	7	7.48	−0.3	7.50	0	10	0
	8	7.51	0.1	7.51	0.01	10	0
	9	7.50	0	7.51	0.01	10	0
	10	7.49	−0.1	7.50	0	10	0

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