利用关联关系的标准件参数化设计方法

分析了产品几何建模的参数化设计方法,提出了一种基于关联关系的标准件参数化设计方法。阐述了参数化设计方法的原理,即采用关联参数建立多个组件间的关联,以实现标准件的参数化设计。利用CATIA平台,给出了圆锥滚子轴承基于关联关系的参数化设计过程,证明了该设计方法的可行性。     

参数化系统设计

该文结合工程实际,根据参数化设计理论设计开发出参数化系统,提出了支持参数化设计的数据结构,参数化设计中的参数等级划分的概念及参数化图形分级降解、递归处理的方法,解决了系统开发过程中的一些问题,并对传统的参数化系统设计方案做了改进。     

基于VBA的AutoCAD参数化设计

阐述了基于CAD模型的参数化设计方法,介绍了参数化设计的基本原理和方法,并阐述了基于AutoCADVBA参数化设计以提高设计效率.     

产品广义参数化设计的研究

分析了参数化设计的研究现状,提出了面向设计周期的产品广义参数化设计的概念,建立了从产品概念设计到详细设计的广义参数化信息模型,定义不同层次的参数映射和约束表达方法,通过主参数来封装产品几何信息和非几何信息,从而最终实现产品级、部件级与零件的参数化设计。     

薄膜蒸发器远程参数化设计技术研究

针对单机版薄膜蒸发器参数化设计系统的诸多局限性,研究了B/S模式远程参数化设计的基本原理及实现方法,并开发了基于WebClass的薄膜蒸发器远程参数化设计系统,对实现其它过程设备的远程参数化设计有一定的参考价值。     

基于COM技术的机构参数化设计系统及其在机构运动仿真中的应用

对于传统机构参数设计,提出了利用COM组件技术对参数设计模块化。将有关公式、技术信息模块化,简化了复式布料系统机构参数设计方面的工作,保存了设计结果,并通过三维图形仿真的方式验证机构设计方案的合理性和各构件在运动过程中是否发生干涉,机构参数设计是否最优。     

参数化图符设计

参数化图符设计在ＣＡＤ中是一种常用的设计方法,本文在分析了一些图符设计方法的基础上,提出了一种简单、实用的参数化图符设计方法－变量求解的参数化图符设计方法,并做了简单分析。     

孔隙表征参数驱动的TPMS多孔结构建模

为了设计符合工程设计参数要求的多孔结构模型,提出一种孔隙表征参数驱动的多孔结构建模思路,并以增材制造制备成形.首先,针对三周期极小化曲面(TPMS)的4种常用类型(P/D/G/I-WP),研究了TPMS数学参数对各类孔隙表征参数(孔隙率、比表面积和孔径大小)的影响,并建立了相关参数之间的映射关系模型;接着,提出了若干基于孔隙表征参数的TPMS多孔结构设计方法,包括单设计参数法、多设计参数法、嵌套设计法;最后,给出了相关的工程应用案例并进行Micro-CT测试实验.实验结果表明,所设计模型的孔隙表征参数可控,增材制造成形的模型样件受工艺影响,其精度存在一定偏差.     

某型发动机架的CATIA参数化设计

参数化设计在产品的三维设计中有着重要的地位,可以提高产品设计效率,简化修改操作。本文就结合某型发动机架的CATIA参数化设计,分析阐述TCATIA参数化设计的方法理念,总结了参数化设计的一般过程,对运用CATIA进行产品研发的工程技术人员具有指导意义。     

线性系统对偶Luenberger观测器设计

提出了线性系统对偶Luenberger观测器的参数化设计方法.基于一类广义Sylvester矩阵方程的显式参数化解,根据一些自由参数给出了对偶Luenberger观测器所有增益矩阵的参数化表达.该设计方法能提供所有的设计自由度,它们能进一步用来实现系统的其他性能指标.数值例子显示了该方法的设计过程.     

高阶滑模微分器的分析与改进

首先采用Terminal吸引子函数代替原不连续函数,以避免滑模微分器的抖振;然后针对现有文献中高阶滑模微分器设计参数选取苛刻这一问题,放宽了设计参数选择范围,并分不同情况证明了高阶滑模微分器的稳定性．在此基础上,给出了高阶滑模微分器估计误差的上界与设计参数和滑模微分器阶次的关系式;通过对设计参数的选取和系统整体结构的分析,提出了高阶滑模微分器减小估计误差且更适于工程应用的设计方法,并给出所需条件．仿真结果表明了所得结论的正确性和有效性．     

Robustness and performance parameterization of smooth second order sliding mode control

Novel robustness and performance parameters are established for Smooth Super Twisting Algorithm (SSTA). The stability of SSTA is well established for arbitrary gains using homogeneity approach. The design and tuning of the controller parameters is a major issue and no analytic design method is available so far. A novel Lyapunov function is proposed and by the virtue of stability analysis, the stability bounds for a certain class of uncertainties are determined. In addition, the issue of finite time convergence is also explored, resulting in determination of the settling time as a function of the controller parameters. The proposed settling time formulation suggests a methodical approach to SSTA design in contrast to the available rules of thumb. Unlike the literature available for Higher Order Sliding Mode (HOSM) controllers, the proposed design framework is validated against a challenging problem of the Underground Coal Gasification (UCG) process control. Like the other process control problems the chosen problem is nonlinear and contains significant uncertainties.     

Adaptive regulation of nonlinear systems with unmodeled dynamics

A feedback linearization design is presented which includes unknown parameters and unmodeled dynamics. An adaptive update law which counteracts the effects of unknown parameters is shown to be robust to the unmodeled dynamics. The proposed design methodology is based on a conceptually simple stability analysis. Conditions are given for global stability of an adaptive control law designed for the reduced-order model of a class of nonlinear plants. In the presence of unmodeled dynamics, the regulation property is preserved in a stability region. The size of the region is estimated using bounds that not only prove robustness, but also allow a comparison between adaptive and nonadaptive nonlinear controls     

PID controller design for nonlinear systems represented by discrete-time local model networks

This paper deals with proportional–integral–derivative (PID) controller design for nonlinear systems represented by local model networks. The proposed method is based on the concept of parallel distributed compensators where the scheduling of the local model network is adopted for the PID parameters. The proposed design method for nonlinear PID controllers considers closed-loop stability by means of a Lyapunov stability criterion as well as closed-loop performance. All PID parameters are determined by a multi-objective genetic algorithm (multiGA), which handles the trade-off between stability and performance. A simulation example demonstrates the effectiveness of the proposed method.     

Robust stability with structured real parameter perturbations

This paper considers the problem of robust stabilization of a linear time-invariant system subject to variations of a real parameter vector. For a given controller the radius of the largest stability hypersphere in this parameter space is calculated. This radius is a measure of the stability margin of the closed-loop system. The results developed are applicable to all systems where the closed-loop characteristic polynomial coefficients are linear functions of the parameters of interest. In particular, this always occurs for single-input (multioutput) or single-output (multiinput) systems where the transfer function coefficients are linear or affine functions of the parameters. Many problems with transfer function coefficients which are nonlinear functions of physical parameters can be cast into this mathematical framework by suitable weighting and redefinition of functions of physical parameters as new parameters. The largest stability hyperellipsoid for the case of weighted perturbations and a stability polytope in parameter space are also determined. Based on these calculations a design procedure is proposed to robustify a given stabilizing controller. This algorithm iteratively enlarges the stability hypersphere or hyperellipsoid in parameter space and can be used to design a controller Io stabilize a plant subject to given ranges of parameter excursions. These results are illustrated by an example.     

比例积分型广义预测控制系统的稳定性分析

广义预测控制(GPC)系统的闭环稳定性一直是控制理论分析的难点．本文通过对预测控制系统闭环特征多项式的研究,证明了一步预测比例积分型GPC系统的闭环稳定性;同时,利用根轨迹法分析了控制参数与闭环极点的关系,明确了比例积分型GPC中参数的物理意义．通过一个数值仿真例子,从频域分析的角度说明了比例积分型GPC较普通GPC的优越性．     

直升机强跟踪自适应变稳控制器设计

变稳控制是直升机飞行模拟的关键技术之一, 气动参数的不确定性是其设计的困难所在. 对此, 提出了利用双目标直升机模型构造原型直升机的强跟踪自适应变稳控制器, 它将强跟踪的反馈补偿机构和自适应控制参数设计相分离, 简化了控制设计, 同时实现了提高自适应参数修正过程的系统稳定裕度和显著降低跟踪误差的目的. 仿真实验结果验证了所提出方法的有效性和可行性.     

Time-varying discrete-time linear systems with bounded rates of variation: Stability analysis and control design

This paper investigates the problems of robust stability analysis and state feedback control design for discrete-time linear systems with time-varying parameters. It is assumed that the time-varying parameters lie inside a polytopic domain and have known bounds on their rate of variation. A convex model is proposed to represent the parameters and their variations as a polytope and linear matrix inequality relaxations that take into account the bounds on the rates of parameter variations are proposed. A feasible solution provides a parameter-dependent Lyapunov function with polynomial dependence on the parameters assuring the robust stability of this class of systems. Extensions to deal with robust control design as well as gain-scheduling by state feedback are also provided in terms of linear matrix inequalities. Numerical examples illustrate the results.     

Global exponential stability of recurrent neural networks forsynthesizing linear feedback control systems via pole assignment

Global exponential stability is the most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements     

Robust control of nonlinear systems with parametric uncertainty

Probabilistic robustness analysis and synthesis for nonlinear systems with uncertain parameters are presented. Monte Carlo simulation is used to estimate the likelihood of system instability and violation of performance requirements subject to variations of the probabilistic system parameters. Stochastic robust control synthesis searches the controller design parameter space to minimize a cost that is a function of the probabilities that design criteria will not be satisfied. The robust control design approach is illustrated by a simple nonlinear example. A modified feedback linearization control is chosen as controller structure, and the design parameters are searched by a genetic algorithm to achieve the tradeoff between stability and performance robustness.