感应式球形电机结构设计与参数分析

针对目前多自由度电机结构复杂,控制难度高的问题,提出一种新型多自由度球形感应电机。该电机由三个空间对称分布的弧面定子驱动球形转子实现多自由度运行。首先,介绍了电机的基本结构和运行原理并对根据直线感应电机的设计理对电机主要参数进行计算;然后,建立Ansoft/Maxwell有限元仿真模型对电机进行稳态磁场仿真;同时对电机在不同转子参数以及不同激励时的稳态运行特性进行分析,为这类电机的优化设计与运动控制提供依据。结果表明:在额定激励下,感应式球形电机单个定子可产生1N·m的稳态转矩,稳定转速可达350r/min,满足多自由度运行要求。     

基于田口法的双余度永磁同步电机优化设计

双余度永磁同步电机有两个余度单元,具有容错率高、体积小、功率密度大等特点,在航空航天、国防、机器人等领域得到广泛应用。针对双余度电机的多目标优化设计问题,基于其典型工作模式,提出二次田口法迭代的多目标优化设计方法。首先,依据双余度度永磁同步电机的应用需求,确定优化目标参数;在初始设计参数基础上,筛选出对目标优化参数影响大的尺寸参数作为优化因子。其次,首次应用田口法筛选出双余度模式下的准最优参数;进而在单余度模式下再次应用田口法对准最优参数进行二次优化;综合两次优化结果确定最终优化参数。最后,通过有限元仿真分析对优化前后的目标参数变化进行验证分析,证明了所提出方法的有效性,也拓宽了对于该类电机的多目标优化设计思路。     

基于平差优化技术的高精度三维标定方法

为提高工业机器人、自动化校准设备等智能系统的整体精度,基于罗德里格矩阵和最小二乘法拟合建立数学模型,解决了三维直角坐标系之间转换关系的工程应用问题。采用基于冗余测量数据的平面平差优化技术和定位坐标点的间接测量方法,提升了局部坐标系建立的精度。针对坐标点接触式测量和非接触式测量的技术缺陷,提出了采用激光跟踪仪测量终端,用标定工装辅助实现间接测量的方法,能通过多点的接触式间接测量保证被测定位坐标点的精度。最后经过多次试验验证了方法的可行性。     

Synthesis and characterization of LiNi0.95-xCoxAl0.05O2 for lithium-ion batteries

Samples of LiNi0.95-xCoxAl0.05O2 （x = 0.10 and 0.15） and LiNiO2, synthesized by the solid-state reaction at 725℃ for 24 h from LiOH-H2O, Ni2O3, Co2O3, and AI（OH）3 under an oxygen stream, were characterized by TG-DTA, XRD, SEM, and electrochemical tests. Simultaneous doping of cobalt and aluminum at the Ni-site in LiNiO2 was tried to improve the cathode performance for lithium-ion batteries. The results showed that co-doping （especially, 5 at.% A1 and 10 at.% Co） definitely had a large beneficial effect in increasing the capacity （186.2 mA.h/g of the first discharge capacity for LiNio.s.42OoaoAlo.0502） and cycling behavior （180.1 mA-h/g after 10 cycles for LiNio.85CooaoAlo.osO2） compared with 180.7 mA.h/g of the first discharge capacity and 157.7 mA.h/g of the tenth discharge capacity for LiNiO2, respectively. Differen- tial capacity versus voltage curves showed that the co-doped LiNio.95_xCoxmlo.osO2 had less intensity of the phase transitions than the pristine LiNiO2.     

A Human–Computer Cooperation Fuzzy c‐Means Clustering with Interval‐Valued Weights

In this paper, a fuzzy c‐means clustering algorithm based on interval‐valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval‐valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process (AHP) method, and then they are transformed into a constraint condition associating with each weight variable in the weighted clustering objective function. In the sequence, the weighted clustering objective function is solved by combining the Lagrange multiplier method with the gradient‐based iteration computation. In the whole process of algorithm iteration, a compulsion strategy with human–computer cooperation is adopted to ensure each weight variable satisfies interval constraint itself. Three well‐known data set are used to perform profound experiments. Experimental results clearly show that the proposed algorithm has better clustering performance than other the weighted fuzzy c‐means clustering algorithm.     

Mooring system diagnosis and structural reliability control for position moored vessels

Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinised and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliability-based set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.     

Supervised Distance Preserving Projections: Applications in the quantitative analysis of diesel fuels and light cycle oils from NIR spectra

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations. Motivated by continuity preservation, the SDPP is a linear projection method where the proximity relations between points in the low-dimensional subspace mimic the proximity relations between points in the response space. Such a projection facilitates the design of efficient regression models and it may also uncover useful information for visualisation. An experimental evaluation is conducted to show the performance of the SDPP and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. The regression step after projection is performed using computationally light models with low maintenance cost like Multiple Linear Regression and Locally Linear Regression with k-NN neighbourhoods. For the evaluation, a benchmark and a full-scale calibration problem are discussed. The case studies pertain the estimation of a number of chemico-physical properties in diesel fuels and in light cycle oils, starting from near-infrared spectra. Based on the experimental results, we found that the SDPP leads to parsimonious projections that can be used to design light and yet accurate estimation models.     

Improved exponential observer design for one‐sided Lipschitz nonlinear systems

This paper investigates the exponential observer design problem for one‐sided Lipschitz nonlinear systems. A unified framework for designing both full‐order and reduced‐order exponential state observers is proposed. The developed design approach requires neither scaling of the one‐sided Lipschitz constant nor the additional quadratically inner‐bounded condition. It is shown that the synthesis conditions established include some known existing results as special cases and can reduce the intrinsic conservatism. For design purposes, we also formulate the observer synthesis conditions in a tractable LMI form or a Riccati‐type inequality with equality constraints. Simulation results on a numerical example are given to illustrate the advantages and effectiveness of the proposed design scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

A Lyapunov‐based small‐gain approach on design of triggering conditions in event‐triggered control systems

This paper studies a Lyapunov‐based small‐gain approach on design of triggering conditions in event‐triggered control systems. The event‐triggered control closed‐loop system is formulated as a hybrid system model. Firstly, by viewing the event‐triggered control closed‐loop system as a feedback connection of two smaller hybrid subsystems, the Lyapunov‐based small‐gain theorems for hybrid systems are applied to design triggering conditions. Then, a new class of triggering condition, the safe, adjustable‐type triggering condition, is proposed to tune the parameters of triggering conditions by practical regulations. This is conducive to break the restriction of the conservation of theoretical results and improve the practicability of event‐triggered control strategy. Finally, a numerical example is given to illustrate the efficiency and the feasibility of the proposed results. Copyright © 2015 John Wiley & Sons, Ltd.