模型参数不确定的多响应参数和容差并行设计

针对模型参数不确定性问题,提出了一种考虑质量损失和制造成本的多响应参数和容差并行设计策略。首先,根据模型参数不确定与设计变量容差,构建多响应的质量损失函数;其次,通过试验数据建立容差成本模型,进而构建参数和容差并行设计的优化目标函数;然后,根据多目标优化算法得到最优参数和容差的Pareto解集,并采用单因素多元方差分析进行容差配置;最后,通过实际案例分析表明,该方法不仅改善了模型的预测性和稳健性能,而且获得了质量损失与制造成本之间的最佳平衡点,与传统的方法相比,能够在显著降低总成本的同时提高产品质量。

不确定半参数模型的Bernstein多项式估计

假设样本观测值是不精确的,通过将不精确的观测值建模为不确定变量,这篇论文提出单调半参数模型的不确定统计推断.单调Bernstein多项式近似非参数函数,利用二次规划算法进行求解.并通过数值例子说明所提出的方法.

不可测输入和模型参数的可观性分析与观测

状态观测器是解决变量不可测问题的有效手段,可用于不可测输入和不确定模型参数的在线估计。基于非线性微分-代数系统的动态模型,将不可测输入和不确定模型参数扩展为系统的状态变量,构造状态观测器对其进行在线观测,给出了满足一定条件下的不可测输入和不确定模型参数可观性的简化条件。以催化裂化装置提升管反应器为例进行分析和仿真,当不可测输入和不确定模型参数同时出现时,仅靠提升管出口一个温度测点将不能保证系统的可观性,需要增加温度测点,才能完成对整个系统不可测变量的观测。

基于平方和的一类不确定非线性系统的鲁棒稳定性

针对模型参数不确定的一类非线性系统,用基于平方和的方法设计了非线性状态反馈控制器。基于平方和的方法可以利用半正定问题将搜索李亚谱诺夫方程及非线性控制器结合在一起,该方法比一般近似线性化或者反馈线性化保守性更低。仿真结果表明了所设计控制器的有效性和可行性。

航空发动机参数不确定模型辨识方法

针对航空发动机线性模型在工况点附近使用范围较小的问题,提出一种航空发动机不确定性模型辨识方法。该方法使用非线性规划处理航空发动机模型辨识问题,求解过程考虑线性模型中矩阵参数不确定性的影响,以期获得一个具有适用范围大、形式简单的航空发动机模型。使用该方法对DGEN380发动机在某一稳态点进行辨识,定义实际工况偏差参数和模型最大偏差参数分析DGEN380发动机参数不确定模型的误差范围。使用实验数据与参数不确定模型仿真结果进行比较,结果表明油门杆角度变化小于22%时,参数不确定模型与实际发动机状态的偏差量较小,能够在1%误差范围内模拟实际发动机状态。

水下滑翔机纵倾运动的自适应积分反演控制

针对浮力驱动式水下滑翔机动力学模型及参数的不确定性,研究了滑翔机纵倾运动跟踪控制问题。首先建立俯仰角与滑块位移的关系,综合应用Lyapunov方法和反演技术设计了非线性自适应跟踪控制器,用自适应机制克服了模型参数的不确定性,在反演镇定函数中引入了积分项,进一步提高了消除稳态跟踪误差的能力,进而将得到的滑块位移作为参考输入,利用反演法设计滑块伺服控制器,使水下滑翔机能够全局渐近跟踪俯仰角参考指令。基于Lyapunov理论证明了系统的稳定性及跟踪误差的收敛性。仿真结果表明,该方法验证了控制器的全局渐近稳定性及对模型不确定参数的自适应性。

考虑预测响应值波动的多响应优化设计

在多响应优化设计中,模型参数的不确定性以及生产过程的噪声因子不可避免地会导致预测响应值出现较大的波动。针对上述的问题,结合贝叶斯抽样技术、帕累托优化策略以及灰色关联分析方法提出了一种多响应优化设计方法。首先,考虑模型参数的不确定性,运用贝叶斯多元回归模型构建了过程响应与试验因子之间的函数关系;其次,根据帕累托最优策略求出了帕累托最优前沿,并计算各试验点达到帕累托最优的贝叶斯后验概率;然后,利用灰色关联分析方法识别出最佳的优化设计方案;最后,实际案例研究表明,在考虑预测响应值波动时,所提的方法能够获得更为稳健和可靠的优化结果。

基于自适应积分反步的四旋翼飞行器控制

四旋翼飞行器系统是强耦合、多输入多输出(MIMO)和非线性的。首先进行动力学建模,考虑模型参数确定与阵风干扰两种情况;然后提出了一种自适应积分反步控制方法应用于飞行器跟踪期望轨迹,整个控制系统采用双闭环回路结构,内回路用于控制姿态,外回路用于稳定位置;最后在模型参数确定的情况下,与积分反步法(integral backstepping,IB)进行实验对比。在模型参数不确定情况下,对飞行器的期望姿态和位移进行跟踪,结果表明,应用自适应积分反步(adaptive integral backstepping,AIB)控制算法的飞行器对外界较强阵风干扰和模型参数不确定具有一定的鲁棒性,能够较为精确地完成轨迹跟踪任务。

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

Uncertainty Assessment of Soil Erosion Model Using Particle Filtering

Recent advances in computer with geographic information system(GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space.However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes.Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty.To deal with the uncertain parameters of a catchment-scale soil erosion model(CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling(PF) is introduced in the CSEM.The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets.PF provides timevarying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set.CSEM-PF was applied to a small mountainous catchment of the Yongdamdam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events.Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.

不确定线性微分对策的鲁棒性及设计

由于存在各种干扰、噪声和恶劣环境,以及模型也不应过分精确的实际,所以考虑微分对策的鲁棒性是必然和重要的.主要就微分对策中存在随机干扰和模型本身参数不确定这两类不确定性问题研究其鲁棒对策问题,并给出其相应的解.

Fuzzy finite element model updating of bridges by considering the uncertainty of the measured modal parameters

It is significant to consider the effect of uncertainty of the measured modal parameters on the updated finite element(FE) model,especially for updating the FE model of practical bridges,since the uncertainty of the measured modal parameters cannot be ignored owing to the application of output-only identification method and the existence of the measured noise.A reasonable method is to define the objective of the FE model updating as the statistical property of the measured modal parameters obtained by conducting couples of identical modal tests,however,it is usually impossible to implement repeated modal test due to the limit of practical situation and economic reason.In this study,a method based on fuzzy finite element(FFM) was proposed in order to consider the effect of the uncertainty of the measured modal parameters on the updated FE model by using the results of a single modal test.The updating parameters of bridges were deemed as fuzzy variables,and then the fuzzification of objective of the FE model updating was proposed to consider the uncertainty of the measured modal parameters.Finally,the effectiveness of the proposed method was verified by updating the FE model of a practical bridge with the measured modal parameters.

The role of constant optimal forcing in correcting forecast models

In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model.The COF can also offset the impact of model error caused by stochastic processes.In reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions.Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results.In summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results.Therefore,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.

Sequential Estimate for Generalized Linear Models with Uncertain Number of Effective Variables

For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.