Magnitude bounds of generalized frequency response functions for nonlinear Volterra systems described by NARX model

In order to reveal the relationship between system time domain model parameters and system frequency response functions, new magnitude bounds of frequency response functions for nonlinear Volterra systems described by NARX model are established. The magnitude bound of the nth-order generalized frequency response function (GFRF) can be expressed as a simple n-degree polynomial function of the magnitude of the first order GFRF, whose coefficients are functions of the model parameters and frequency variables. Thus the system output spectrum can also be bounded by a polynomial function of the magnitude of the first order GFRF. These results demonstrate explicitly the analytical relationship between model parameters and system frequency response functions, and provide a significant insight into the magnitude based analysis and synthesis of nonlinear systems in the frequency domain.     

Axial offset control of PWR nuclear reactor core using intelligent techniques

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.     

基于NARX神经网络的汽轮机转子温度变化趋势实时预测

利用退役转子及其对应的实际历史运行工况,开展了基于NARX神经网络的汽轮机转子关键位置温度发展趋势实时预测的研究。利用有限元方法,计算了历史运行工况下转子的高保真温度场,获取了关键位置的温度变化趋势。采用NARX神经网络,构建了转子进口蒸汽温度和功率与转子关键位置温度之间的数学关系,通过将输出参数闭环到输入参数集,形成了闭环神经网络结构,实现实时多步预测。预测结果表明,该模型对温度变化趋势的实时预测效果较好,且可以快速校正误差较大的预测结果。     

Identification of polynomial input/output recursive models with simulation error minimisation methods

Polynomial input/output (I/O) recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature, which deal with both model selection and parameter estimation. Previous works have shown the limitations of the classical prediction error minimisation approach in this context, especially (but not only) when the disturbance contribution is unknown, and suggested the use of a simulation error minimisation (SEM) approach for a better selection of the I/O model. This article goes a step further by integrating the model selection procedure with a simulation-oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can sometimes achieve great performance improvements with respect to previously proposed approaches. Two different parameter estimation algorithms are suggested, namely a direct SEM optimisation algorithm, and an approximate method based on multi-step prediction iteration, which displays several convenient properties from the computational point of view. Several simulation examples are shown to demonstrate the effectiveness of the suggested SEM approaches.     

基于多尺度自回归模型体系的混凝土坝位移预报及工程应用

针对混凝土坝位移监测数据的时频非线性特征严重影响到数值模型预报精度的难题,通过小波技术解析原型数据中多重交叉环境驱动的效应实况,有机结合非线性自回归模型(Nonlinear Autoregressive Model with Exogenous Input, NARX)和差分整合移动平均自回归模型(Autoregressive Integrated Moving Average Model, ARIMA),建立了多尺度组合机制下的自回归模型体系,解决了内蕴复杂混沌特性的监测序列的信息挖掘难点。工程实例分析表明,所建模型的拟合精度及预测能力均得以提升,相比于传统模型具有较好的抗噪性和鲁棒性。此外,所建立的计算模型经一定的优化和拓展,亦可推广应用于其它水工建筑物的效应预报分析。     

An analysis method for correlation between catenary irregularities and pantograph-catenary contact force

Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however, the pantograph-catenary contact force is largely affected by the catenary irregularities. To analyze the correlated relationship between catenary irregularities and pantograph-catenary contact force, a method based on nonlinear auto-regressive with exogenous input （NARX） neural networks was developed. First, to collect the test data of catenary irregularities and contact force, the pantograph/catenary dynamics model was established and dynamic simulation was conducted using MATLAB/Simulink. Second, catenary irregularities were used as the input to NARX neural network and the contact force was determined as output of the NARX neural network, in which the neural network was trained by an improved training mechanism based on the regularization algorithm. The simulation results show that the testing error and correlation coefficient are 0.1100 and 0.8029, respectively, and the prediction accuracy is satisfactory. And the comparisons with other algorithms indicate the validity and superiority of the proposed approach.     

基于多加速度传感器的动态检重秤抗振方法研究

针对检重秤测量过程中受振动干扰影响严重的问题,提出一种基于外部输入非线性自回归模型(NARX)的动态神经网络系统辨识的抗振新方法。通过加速度传感器的冗余分布,对检重秤系统的振动特性进行估计,结合空载传送情况下称重传感器由振动干扰产生的误差,利用动态神经网络对振动干扰信号进行自动辨识,建立振动信号分析模型,用以匹配消除动态检重信号中的振动扰动。在共振状态下,与滑窗滤波、自适应陷波等传统抗振方法进行的仿真与测试实验对比,证明基于多加速度传感器的动态称重抗振性能更优,最终实现运行速度达2 m/s,最大秤量200.0 g,满足国家标准《GB/T 27739-2011自动分检衡器》XIII级要求的检重秤搭建。     

基于正交匹配追踪算法的复杂系统动力学建模与分析

针对非线性系统模型的辨识问题,通过引入正交匹配追踪(Orthogonal matching pursuit,OMP)算法实现快速非线性系统建模。该方法旨在解决非线性有源自回归(Nonlinear autoregressive with exogenous inputs,NARX)模型针对大型数据建模时效性差的问题。首先,说明了正交最小二乘(Orthogonal least squares,OLS)算法存在正交次数多、耗时长的问题,采用OMP算法可有效解决,通过与OLS算法对比正交差异性证明了OMP算法计算效率提升的理论基础,采用模型预报方法验证OMP算法所得NARX模型的动力学特性。其次,以单自由度非线性系统为例,说明了OMP算法系统建模的有效性。最后,利用OMP算法建立悬臂梁NARX模型,并分别将NARX模型预报输出与试验实测输出,NARX模型固有频率与悬臂梁实际固有频率进行对比。结果表明,与OLS算法相比,所提方法的建模效率平均提升了10倍,且模型可有效反应系统动力学特性。     

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.