EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer
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摘要:
针对电静液作动器(EHA)功率密度高、工况复杂、元件集成度高、故障种类多的特点,设计了一种基于自适应神经网络鲁棒观测器的电静液作动器故障诊断与容错控制器。对模型的内部状态提出一种鲁棒观测器进行观测,对液压系统弹性模量等不确定性设计参数自适应率进行估计,对摩擦扰动等非线性设计径向基函数(RBF)神经网络予以逼近。通过前馈补偿的方法对故障和参数不确定性进行补偿,同时针对系统其他扰动设计鲁棒项加以克服。利用Lyapunov稳定性定理证明了所提出的控制器在存在故障的情况下可以实现系统的有界稳定。联合仿真结果表明:相对于传统的比例、积分、微分控制器(PID)和自适应鲁棒控制器(ARC),所提出的控制器具有更高的控制精度与鲁棒性。
Abstract:Aiming at the characteristics of high power density, complex working conditions, high integration of components and the wide variety of faults of electro hydrostatic actuator (EHA), a fault diagnosis and fault-tolerant controller of electro-hydro actuator based on an adaptive neural network robust observer is designed. A robust observer is proposed to observe the internal state of the model. The uncertain parameters, such as elastic modulus of hydraulic system is estimated by adaptive law; and the nonlinear, such as friction disturbance is approximated by radial basis function (RBF) neural network. The feedforward compensation method is used to compensate the fault and parameter uncertainty, and the robust term is designed to overcome other disturbances. By using Lyapunov stability theorem, it is proved that the proposed controller can realize the bounded stability of the system in the presence of faults. The co-simulation results show that the proposed controller has higher control accuracy and robustness than the traditional proportional, integral and differential controller (PID) and adaptive robust controller (ARC).
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表 1 正弦信号工况下的性能指标
Table 1. Performance index under sinusoidal signal condition
控制器 $M_{{\rm{e}}}$ $ \mu $ $ \sigma $ PID 0.008 948 3.456×10−5 0.004 752 ARC 2.0×10−6 0.0001234 3.862×10−5 BP-FTARC 1.4×10−7 2.079×10−9 9.09×10−8 RBF-FTARC 8.355×10−8 8.619×10−10 6.033×10−8 表 2 联合仿真系统参数
Table 2. Parameter of co-simulation system
参数 数值 液压缸活塞和负载总质量$M$/kg 30 黏滞阻尼系数$B$/(N·(m·s−1)−1) 400 液压缸活塞面积$A$/${{\rm{m}}^2}$ 904.78×10−6 液压管路和油缸平均容积${V_{\rm{a} } }/{ { {\rm{m} }^{3} } }$ 398.1×10−7 等效弹性模量${\beta _{\rm{e}}}/ {{\rm{MPa}}}$ 700×106 泄漏系数$/({ {\rm{L·(min·MPa)} }^{-1} } )$ 1×10−12 泵排量${D_{\rm{p} } }/( { { {\rm{cm} }^{ 3} }\cdot{\rm{r}^{-1} } } )$ 19×10−6 表 3 工况1下各控制器性能指标
Table 3. Performance index of each controller under condition 1
控制器 $M_{{\rm{e}}}$ $ \mu $ $ \boldsymbol{\sigma} $ PID 0.008 399 0.000 190 5 0.005 632 ARC 0.001 334 6.89×10−6 0.000 319 BP-FTARC 0.000 562 3 2.835×10−6 0.000 336 RBF-FTARC 0.000 409 3 2.625×10−6 0.000 176 表 4 工况2下各控制器性能指标
Table 4. Performance index of each controller under condition 2
控制器 $M_{{\rm{e}}}$ $ \mu $ $ \boldsymbol{\sigma} $ PID 0.01362 5.979×10−5 0.008 244 ARC 0.01763 3.603×10−6 0.002 419 BP-FTARC 0.004 623 2.364×10−6 0.001 556 RBF-FTARC 0.001 997 2.187×10−6 0.001 126 表 5 工况3下各控制器性能指标
Table 5. Performance index of each controller under condition 3
控制器 $M_{{\rm{e}}}$ $ \mu $ $ \sigma $ PID 0.034 58 0.000 493 5 0.01048 ARC 0.037 86 −3.829×10−6 0.003 736 BP-FTARC 0.006 982 −1.75×10−6 0.002 349 RBF-FTARC 0.003 972 9.379×10−7 0.000 638 2 表 6 工况4下各控制器性能指标
Table 6. Performance index of each controller under condition 3
控制器 $M_{{\rm{e}}}$ $ \mu $ $ \sigma $ PID 0.023 41 5.949×10−5 0.007 908 ARC 0.031 19 −3.605×10−6 0.000 855 2 BP-FTARC 0.007 868 1.759×10−6 0.002 65 RBF-FTARC 0.003 789 −9.339×10−7 0.000 650 2 -
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