Online stiffness estimation for robotic tasks with force observers

In this paper we propose an online stiffness estimation technique for robotic tasks based only on force data, therefore, not requiring contact position information. This allows estimations to be obtained in robotic tasks involving interactions with unstructured and unknown environments where geometrical data is unavailable or unreliable. Our technique – the Candidate Observer Based Algorithm (COBA) – uses two force observers, configured with different candidate stiffnesses, to estimate online the actual target object stiffness. COBA is embedded in a force control architecture with computed torque in the task space. The theoretical presentation of the algorithm, as well as simulation tests and experimental results with a lightweight robot arm are also presented.     

一种关于目标罚参数的精确罚函数法

罚函数法是一种将约束优化问题转化为无约束问题的重要方法.对于一般的约束优化问题,通过加入新参数,给出了一种改进的精确罚函数和这种罚函数的精确罚定理证明,提出了求解这种罚函数的算法.实验表明该算法是有效的.     

基于双EnKF的土壤水分与土壤属性参数同时估计

为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.     

A multi-criteria trajectory-based parameter sampling strategy for the screening method of elementary effects

Environmental models are inherently complex and often characterized by high dimensionality. The method of elementary effects (EE) is one of the most widely used parameter screening technique implemented to reduce burden on computational resources required for thorough model evaluation. Due to issues like inefficient screening and excessive sampling time, the development of more effective EE sampling strategies has been a recent research focus. This paper presents a new sampling strategy - Sampling for Uniformity (SU) – based on the principles of meeting close-to-theoretical parameter distributions and maximizing trajectory spread. The performance of the SU relative to existing strategies was evaluated using a number of criteria including generated parameter distributions' uniformity, time efficiency, trajectory spread, and screening efficiency. The SU performed better than some trajectory-based benchmark strategies across the evaluation criteria, underlining the effectiveness of multi-criteria based sampling and the need to focus future efforts on exploring other combinations of sampling criteria.     

基于有限元法的E-K模型参数敏感性分析

模型参数的确定是数值分析过程中的重要环节,合理把握参数的选取原则,可大大提高结构分析的可靠度以及参数反分析的计算效率.本文借助有限单元法考察了E-K模型参数变化对一个均质堤坝的位移和应力值的影响程度.通过分析明确了E-K模型中各参数对计算结果的敏感程度,同时提出了模型参数的选取原则.     

Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms

Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model parameters which represent the PV device. In the present work, parameter estimation for the model parameter using various evolutionary algorithms is presented and compared. The constraint set on the estimation process is that only the data directly available in module datasheets can be used for estimating the parameters. The electrical model accuracy using the estimated parameters is then compared to several electrical models reported in literature for various PV cell technologies.     

基于ROC曲线寻优的支持向量机性能研究

支持向量机在小样本模式识别领域具有优势,但其性能评估及核参数、正则化参数的选择尚未有标准算法.将受试者操作特性曲线(Receiver Operating Characteristic,ROC)引入支持向量机分类性能分析和建模参数优化问题.在核参数及正则化参数所构成的二维空间中,调整模型参数阈值描绘ROC曲线,通过比较不同分类器ROC曲线下面积实现模型的性能分析,研究了基于ROC曲线最佳工作点的模型优化问题.工程实例表明,ROC曲线下面积有效地量化了模型的识别性能,并给出了一定寻优范围内的模型参数最优点,可以在SVM模型参数优化问题中推广应用.     

Parameter augmentation and the q-Gosper algorithm

We develop a method for deriving new basic hypergeometric identities from old ones by parameter augmentation. The main idea is to introduce a new parameter and use the q-Gosper algorithm to find out a suitable form of the summand. By this method, we recover some classical formulas on basic hypergeometric series and find extensions of the Rogers–Fine identity and Ramanujan’s summation formula. Moreover, we derive an identity for a summation.