| 引用本文: | 毛定坤,蔡光斌,冯志超,侯明哲,班晓军.变体飞行器传感器故障在线主动容错控制[J].哈尔滨工业大学学报,2023,55(8):60.DOI:10.11918/202205056 |
| MAO Dingkun,CAI Guangbin,FENG Zhichao,HOU Mingzhe,BAN Xiaojun.Online active fault tolerant control for sensor fault of morphing aircraft[J].Journal of Harbin Institute of Technology,2023,55(8):60.DOI:10.11918/202205056 |
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| 变体飞行器传感器故障在线主动容错控制 |
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毛定坤1,蔡光斌1,冯志超1,侯明哲2,班晓军2
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(1.火箭军工程大学 导弹工程学院,西安 710025; 2.哈尔滨工业大学 航天学院,哈尔滨 150001)
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| 摘要: |
| 为保证变体飞行器在传感器故障情况下的稳定飞行性能和良好跟踪效果,提高故障诊断准确度和容错控制能力,针对变体飞行器传感器故障诊断与容错控制问题,提出一种基于考虑属性可靠度的置信规则库(BRB-r)专家系统在线主动容错控制方法。首先,给出变体飞行器的气动参数模型和纵向非线性动力学模型,综合考虑外界扰动和传感器故障,利用最小二乘拟合方法和雅克比线性化方法,建立变体飞行器的切换线性变参数(LPV)故障模型；然后,基于BRB-r专家系统构建变体飞行器传感器故障诊断与容错控制模型,通过统计方法对传感器监测指标进行可靠性分析,并引入证据推理(ER)解析算法,提高故障诊断精度和容错控制效果；最后,利用基于投影算子的协方差矩阵自适应优化策略(P-CMA-ES)算法优化故障诊断与容错控制模型,降低系统的复杂度,提高了故障诊断效率。仿真结果表明,变体飞行器传感器故障诊断精度能够达到98.75%,当传感器故障程度小于50%时,所提方法能够有效克服传感器故障和外界扰动,保证变体飞行器的稳定飞行,具有较强的容错控制能力和鲁棒性能。 |
| 关键词: 变体飞行器 故障诊断 容错控制 传感器故障 BRB-r专家系统 |
| DOI：10.11918/202205056 |
| 分类号:V249.1 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(7,6) |
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| Online active fault tolerant control for sensor fault of morphing aircraft |
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MAO Dingkun1,CAI Guangbin1,FENG Zhichao1,HOU Mingzhe2,BAN Xiaojun2
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(1.School of Missile Engineering, Rocket Force University of Engineering, Xian 710025, China; 2.School of Astronautics, Harbin Institute of Technology, Harbin 150001, China)
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| Abstract: |
| To ensure the stable flight performance and good tracking effect of the morphing aircraft in the case of sensor fault, and improve the accuracy of fault diagnosis and fault-tolerant control capability, an online active fault-tolerant control method based on belief rule base (BRB-r) expert system considering attribute reliability is proposed highly specific for the sensor fault diagnosis and fault-tolerant control of the morphing aircraft. Firstly, the aerodynamic parameter model and longitudinal nonlinear dynamic model of morphing aircraft are presented. Considering the external disturbance and sensor fault, the switched linear parameter varying (LPV) fault model of morphing aircraft is established by using the least fitting method and Jacobian linearization method. Then, based on BRB-r expert system, a sensor fault diagnosis and fault-tolerant control model of morphing aircraft is constructed. The reliability of sensor monitoring index is analyzed by statistical method, and evidence reasoning (ER) algorithm is introduced to improve the accuracy of fault diagnosis and fault-tolerant control effect. Finally, the projection operator covariance matrix adaptive optimization strategy (P-CMA-ES) algorithm is used to optimize the fault diagnosis and fault tolerant control model, which reduces the complexity of the system and improves the efficiency of fault diagnosis. The simulation results show that the sensor fault diagnosis accuracy of the morphing aircraft can reach 98.75%. When the sensor fault degree is less than 50%, the proposed method can effectively overcome the sensor fault and external disturbance, ensuring the stable flight of the morphing aircraft, and exhibiting strong fault-tolerant control capability and robust performance. |
| Key words: morphing aircraft fault diagnosis fault tolerant control sensor fault BRB-r expert system |
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