共查询到18条相似文献,搜索用时 203 毫秒
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针对固定遗忘因子递推最小二乘法(RLS)在永磁同步电机参数识别中难以同时保证快速性和鲁棒性的问题,提出一种动态调节遗忘因子大小的递推最小二乘参数识别算法.分析了遗忘因子对RLS算法的影响特性,以理论模型与实际模型输出的差值为变量构建遗忘因子调节函数,实现遗忘因子动态调整.仿真结果表明,相比于固定遗忘因子RLS算法,改进... 相似文献
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电池荷电状态SOC(State Of Charge)作为电池管理系统中尤为重要的一部分,其准确估计成为锂离子电池研究的重点。为了提高动态工况下的SOC估计精度,对锂离子电池等效模型进行分析,基于AIC(赤池信息)准则确定二阶RC电路为等效电路模型,使用递推最小二乘算法对模型参数进行在线辨识,为提高辨识精度,提出了改进带动态遗忘因子递推最小二乘算法,对算法加入遗忘因子,通过电压结果误差实时动态调整算法遗忘因子取值。将递推最小二乘算法和含动态遗忘因子最小二乘算法分别与扩展卡尔曼滤波(EKF)算法进行SOC联合估计,并对比其预测效果,结果表明含有动态遗忘因子最小二乘与EKF联合估计模型具有更高的精度和鲁棒性。 相似文献
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对于直扩码分多址系统,本文提出了一种新的基于可变遗忘因子RLS的自适应盲多用户检测器,它能够自适应地估计检测向量,既具有对时变信道的快速跟踪能力,又具有较小的估计误差。最后通过仿真验证了该方法的有效性,与固定遗忘因子RLS盲多用户检测器相比,新算法具有更高的输出信干比,并且动态环境下的跟踪能力明显提高。另外还研究了参数对性能的影响,为参数的选择作出参考。 相似文献
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针对广义预测控制(GPC)模型中输入输出数据可能存在噪声和系统先验结构信息未知导致的难于辨识问题,提出了一种子空间辨识的广义预测控制算法。该算法采用变遗忘因子的子空间辨识方法,按照预测优化值与参考输出值的误差构造变遗忘因子,调整采集数据权重,进行在线辨识以提高灵敏度和控制效果。实验结果验证了所提出算法的有效性。 相似文献
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针对使用永磁同步电机作为执行机构的高精度交流调速系统中存在负载惯量时变、转矩扰动和未建模动态的情况,利用带遗忘因子的递推最小二乘算法(FRLS)在线辨识系统时变参数,通过扩张状态观测器(ESO)观测参数辨识误差和未建模动态等非线性因素,设计一种集 PI 控制器、基于 FRLS 的补偿器、基于 ESO 的补偿器和鲁棒控制器的复合速度控制器,并分析了闭环调速系统的稳定性.仿真结果验证了该复合速度控制器的有效性. 相似文献
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In this paper, a recursive subspace identification method is proposed to identify linear time-invariant systems subject to load disturbance with relatively slow dynamics. Using the linear superposition principle, the load disturbance response is decomposed from the deterministic-stochastic system response in the form of a time-varying parameter. To ensure unbiased estimation of the deterministic system matrices, a recursive least-squares (RLS) identification algorithm is established with a fixed forgetting factor, while another RLS algorithm with an adaptive forgetting factor is constructed based on the output prediction error to quickly track the time-varying parameter of load disturbance response. By introducing a deadbeat observer to represent the deterministic system response, two extended observer Markov parameter matrices are constructed for recursive estimation. Consequently, the deterministic matrices are retrieved from the identified system Markov parameter matrices. The convergence of the proposed method is analysed with a proof. Two illustrative examples are shown to demonstrate the effectiveness and merit of the proposed identification method. 相似文献
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一种具有快速跟踪能力的改进RLS算法研究 总被引:1,自引:0,他引:1
为了改善固定遗忘因子BLS(Recursive least-square)算法在时变系统中的跟踪性能,提出了一种改进的BLS算法.改进的BLS算法结合了可变遗忘因子的BLS算法和自扰动BLS算法,既克服了固定遗忘因子RLS算法中跟踪速度和参数失调的矛盾,而且也避免了当参数估值趋向于参数真值时,卡尔曼增益趋于零,从而BLS算法失去对时变系统的跟踪能力的问题.最后,在MATLAB平台下,对改进后的RIS算法进行了仿真验证.仿真结果表明,算法具有较快的收敛速度和跟踪速度以及较小的稳态误差. 相似文献
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考虑了多变量输出误差系统的辨识问题. 使用系统可得到的输入输出数据构造一个辅助模型, 用辅助模型的输出代替信息向量中的未知变量, 提出了一个基于辅助模型的随机梯度辨识算法. 使用鞅收敛定理的收敛性分析表明: 提出的算法给出的参数估计收敛于它们的真值. 给出了带遗忘因子的辅助模型随机梯度算法来改进参数估计精度, 仿真结果证实了提出的结论. 相似文献
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在高速水声通信系统中,由于多径传播而引起的码间串扰ISI是传输误码率增加的主要原因。判决反馈均衡器(DFE)是对抗码间串扰ISI的有效方法。为了获得码间串扰为零的传输,固定遗忘因子的最小二乘递归算法(RLS)通常被用于更新DFE的抽头向量,但是这种算法在非稳定环境中并不能取得最佳表现。该文在获取均衡误差最小的即时过程中推导出一种应用在DFE中的自适应遗忘因子RLS算法。计算机仿真证明此方法对比以往的固定遗忘因子算法或可变遗忘因子算法获得了较好的传输表现。 相似文献
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In this paper, a new parallel adaptive self-tuning recursive least squares (RLS) algorithm for time-varying system identification is first developed. Regularization of the estimation covariance matrix is included to mitigate the effect of non-persisting excitation. The desirable forgetting factor can be self-tuning estimated in both non-regularization and regularization cases. We then propose a new matrix forgetting factor RLS algorithm as an extension of the conventional RLS algorithm and derive the optimal matrix forgetting factor under some reasonable assumptions. Simulations are given which demonstrate that the performance of the proposed self-tuning and matrix RLS algorithms compare favorably with two improved RLS algorithms recently proposed in the literature. 相似文献
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In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open- and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method. 相似文献
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球杆系统是一种典型的高阶非线性不稳定系统,针对PID跟踪控制精度不高及BP神经网络控制训练时间较长的问题,本文提出一种带有低通滤波器的RBF神经网络控制器(RBFC)动态补偿PID控制的球杆控制方法,控制系统由RBF神经网络控制及PID控制器组成。为提高参数辨识速度和避免局部最小值,采用梯度下降法更新隐含层参数,采用带有遗忘因子的最小二乘法更新输出层权值。实验结果表明,该控制方案相比PID控制具有更高的控制精度,比BP神经网络具有更快的学习速度,低通滤波器保证了RBFC的辨识精度和稳定的控制输出,具有良好的动静态特性和控制性能。 相似文献
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A neurofuzzy scheme has been designed to carry out on-line identification, with the aim of being used in an adaptive–predictive dynamic matrix control (DMC) of unconstrained nonlinear systems represented by a transfer function with varying parameters. This scheme supplies to the DMC controller the linear model and the nonlinear output predictions at each sample instant, and is composed of two blocks. The first one makes use of a fuzzy partition of the external variable universe of discourse, which smoothly commutes between several linear models. In the second block, a recurrent linear neuron with interpretable weights performs the identification of the models by means of supervised learning. The resulting identifier has several main advantages: interpretability, learning speed, and robustness against catastrophic forgetting. The proposed controller has been tested both on simulation and on a real laboratory plant, showing a good performance. 相似文献