The problem of how to identify the piecewise affine system is studied in this paper, where this considered piecewise affine system is a special nonlinear system. The reason why it is not easy to identify this piecewis...The problem of how to identify the piecewise affine system is studied in this paper, where this considered piecewise affine system is a special nonlinear system. The reason why it is not easy to identify this piecewise affine system is that each separated region and each unknown parameter vector are all needed to be determined simultaneously. Then, firstly, in order to achieve the identification goal, a multi-class classification process is proposed to determine each separated region. As the proposed multi-class classification process is the same with the classical data clustering strategy, the multi-class classification process can combine the first order algorithm of convex optimization, while achieving the goal of the classification process. Secondly, a zonotope parameter identification algorithm is used to construct a set, which contains the unknown parameter vector. In this zonotope parameter identification algorithm, the strict probabilistic description about the external noise is relaxed, and each unknown parameter vector is also identified. Furthermore, this constructed set is consistent with the measured output and the given bound corresponding to the noise. Thirdly, a sufficient condition about guaranteeing our derived zonotope not growing unbounded with iterations is formulated as an explicit linear matrix inequality. Finally, the effectiveness of this zonotope parameter identification algorithm is proven through a simulation example.展开更多
叶端定时(blade tip timing,BTT)技术是当前研究重大装备动叶片状态监测与故障诊断的趋势,但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题,首先,通过快速傅里叶变换(fast Fourier t...叶端定时(blade tip timing,BTT)技术是当前研究重大装备动叶片状态监测与故障诊断的趋势,但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题,首先,通过快速傅里叶变换(fast Fourier transform,FFT)算法提取动叶片异步振动幅值和异步振动频率的非整数阶次;随后,改进现有多信号分类(multiple signal classification,MUSIC)算法,提出基于阶次搜索-多信号分类(engine order search-based multiple signal classification,EOS-MUSIC)算法的动叶片异步振动频率整数阶次搜索策略;最后,融合EOS-MUSIC算法与FFT算法分析结果辨识动叶片异步振动参数。基于MATLAB软件仿真动叶片异步振动信号,与现有MUSIC算法比较,验证了EOS-MUSIC算法的可信性和准确性。在离心压气机试验台开展叶轮叶片振动试验,与应变片法相比,EOS-MUSIC算法频率辨识绝对误差为3.36 Hz,相对误差仅为0.53%。本文在FFT算法预处理的基础上,通过阶次搜索辨识动叶片异步振动参数,克服了现有MUSIC算法搜索周期长和辨识精度低的难题,为动叶片异步振动参数辨识提供了理论支撑。展开更多
文摘The problem of how to identify the piecewise affine system is studied in this paper, where this considered piecewise affine system is a special nonlinear system. The reason why it is not easy to identify this piecewise affine system is that each separated region and each unknown parameter vector are all needed to be determined simultaneously. Then, firstly, in order to achieve the identification goal, a multi-class classification process is proposed to determine each separated region. As the proposed multi-class classification process is the same with the classical data clustering strategy, the multi-class classification process can combine the first order algorithm of convex optimization, while achieving the goal of the classification process. Secondly, a zonotope parameter identification algorithm is used to construct a set, which contains the unknown parameter vector. In this zonotope parameter identification algorithm, the strict probabilistic description about the external noise is relaxed, and each unknown parameter vector is also identified. Furthermore, this constructed set is consistent with the measured output and the given bound corresponding to the noise. Thirdly, a sufficient condition about guaranteeing our derived zonotope not growing unbounded with iterations is formulated as an explicit linear matrix inequality. Finally, the effectiveness of this zonotope parameter identification algorithm is proven through a simulation example.
文摘叶端定时(blade tip timing,BTT)技术是当前研究重大装备动叶片状态监测与故障诊断的趋势,但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题,首先,通过快速傅里叶变换(fast Fourier transform,FFT)算法提取动叶片异步振动幅值和异步振动频率的非整数阶次;随后,改进现有多信号分类(multiple signal classification,MUSIC)算法,提出基于阶次搜索-多信号分类(engine order search-based multiple signal classification,EOS-MUSIC)算法的动叶片异步振动频率整数阶次搜索策略;最后,融合EOS-MUSIC算法与FFT算法分析结果辨识动叶片异步振动参数。基于MATLAB软件仿真动叶片异步振动信号,与现有MUSIC算法比较,验证了EOS-MUSIC算法的可信性和准确性。在离心压气机试验台开展叶轮叶片振动试验,与应变片法相比,EOS-MUSIC算法频率辨识绝对误差为3.36 Hz,相对误差仅为0.53%。本文在FFT算法预处理的基础上,通过阶次搜索辨识动叶片异步振动参数,克服了现有MUSIC算法搜索周期长和辨识精度低的难题,为动叶片异步振动参数辨识提供了理论支撑。
文摘采用自主水下航行器(Autonomous Underwater Vehicle,AUV)磁测平台可开展海洋地磁场测量、水下磁性目标探测和识别等工作,AUV磁测平台具有广阔的应用前景,但目前AUV载体磁干扰补偿技术研究尚不成熟,制约着水下航行器测磁精度。基于磁测平台抗磁干扰基本原理,提出一种基于线性种群规模缩减和成功历史的参数自适应差分进化(Success History-based Adaptive Differential Evolution with Linear Population Size Reduction,L-SHADE)算法的AUV载体磁干扰参数辨识的数值模拟方法。用磁偶极子和旋转椭球壳混合模型来等效模拟AUV载体磁干扰,通过模拟航行获得多组磁测数据,据此建立磁干扰参数辨识模型,并采用L-SHADE算法求解。通过数值模拟实验定量分析研究磁测平台测磁精度随磁传感器、平台姿态及航向等误差的传播规律。研究结果表明:当磁传感器测量精度为10 nT、姿态测量精度为0.01°、航向测量精度为0.1°时,测磁误差可小于100 nT。设计的AUV磁测平台抗干扰试验表明,地磁场总量最大相对误差为1.07%。
