采用多刚体方法对链条链轮系统的驱动过程进行了建模和动力学分析。
This paper deals with the driving process of chain sprocket system with the multi rigid body dynamics method.
虚拟样机技术的核心是机械系统动力学、有限元理论和控制理论等建模理论及其技术的实现。
The core of the virtual prototype technology is on the realization of modeling theories and technology, mechanical system dynamics, finite element theory and control theory, etc.
介绍采用多刚体方法对链条传动系统的驱动过程进行建模和动力学分析。
This paper deals with the driving process of chain-sprocket system with the multi rigid body dynamics method. With the model, the process of the dynamical response is analyzed.
方法具有一般意义，可用于其他复杂机电系统的动力学建模和分析。
The proposed method may be commonly applied to modelling and analysis of other complex electromechanical systems.
建立了这类系统的动力学模型，包括考虑附加质量的一般刚体动力学方程和基于“平衡点”假设的吊挂系统建模方法。
The model is comprised of the general rigid-body equations considering apparent mass and the modeling method of suspension system based on the assumption of "equilibrium points".
引入路的概念，它使多体系统运动学和动力学建模更简洁，使用最少的信息。
The concept of "path" has been introduced to make the formulation for the kinematics and dynamics of multibody system more concise and least information needed.
这种结构的RBFNN特别适用于非线性动力学系统的辨识（建模）和控制。
The improved RBFNNs are suitable to identifying (modelling) or controlling nonlinear dynamic systems.
这种统一建模仿真分析的思想和框架有助于更有效地研究复杂供应链系统的动力学机理。
The thought and framework for the unified modelling, simulation and analysis will help us to more effectively study the dynamics mechanism of complex supply chain systems.
针对高方平筛和其上固结的柔性吊杆组成的动力学系统，进行了刚柔耦合的动力学建模和振动模态分析。
The rigid-flexible coupling dynamics' modeling and modal analysis were employed to study the system consisted of square plansifter and flexible suspender.
由于自主水下航行器建模误差、流体动力学参数的不确定性和未知海流干扰，控制系统必须具有对不确定参数的自适应能力。
The control system must be adaptive to the uncertain parameters to overcome the modeling error, uncertainty of the hydrodynamic parameters and unknown ware disturbances.
建立了有内导向瓣周边式对接机构的两空间飞行器对接过程的动力学模型，建模中将对接系统简化为主动飞行器、被动飞行器和主动捕获环三体问题。
The mathematical models of docking dynamics in the process of capture are established for two spacecrafts with inner guide petal peripheral docking mechanisms.
利用非线性系统的线性反馈理论，考虑动力学建模的模型误差和外界干扰等不确定因素，提出了一种鲁棒轨迹跟踪控制策略。
The robust trajectory tracking control strategy accounts for model errors and external disturbances using linear feedback theory for the non-linear system, which ensures system stability and li.
介绍了一种运用多体动力学进行机械系统仿真的新方法——矢量网络法，包括建模方法和其常规公式化的解题程序。
This paper describes a new method to simulate mechanical systems with the theory of multibody system-vector network method, including the modeling method and the normal formulized procedure.
基于柔性多体动力学理论，对可展开天线进行了系统建模和动力响应计算及分析。
Based on the theory of Flexible multi-body dynamics, the model of deploy-able antenna is established, and the dynamic response and analysis calculation to large space deployable antenna is done.
本文围绕复杂供应链系统的建模仿真和复杂动力学机理的分析展开研究。
In this dissertation, we carry out the research on modelling and simulation of the complex supply chain systems and complex dynamics mechanism of the systems.
根据多柔体系统动力学建模理论以及ADAMS的柔性体建模方法，建立了柔性磁悬浮转子系统的ADAMS、MATLAB和ANSYS联合仿真模型。
According to the flexible multi-body system modeling theory and ADAMS flexible modeling method, the ADMAS, MATLAB and ANSYS combined simulation model of the flexible MSR system is established.
仿真实例分析说明本文所建模型和建模采用的方法对高速履带车辆的悬挂系统的设计和动力学分析具有积极的作用。
Case analysis proved to be acceptable and show that the model and its method can be used for armored tracked vehicle suspension design and dynamical analysis.
本文提出了一种运用指数积公式和凯恩方程对 水下机器人机械手系统进行了动力学建模 的新方法。
The virtual prototype for interfacing apparatus of DSRV has been built and the interfacing process has been done in this paper.
神经网络方法可用于动力学系统的参数估计，包括时域和频域建模。将单层神经网络用于单交量线性连续时不变系统的参数估计中，线性系统分别用状态方程及传递函数来描述。
A simple neural network structure and its learning algorithm are presented for the purpose of system identification of a linear time invariant system which is represented by the state space equation.
神经网络方法可用于动力学系统的参数估计，包括时域和频域建模。将单层神经网络用于单交量线性连续时不变系统的参数估计中，线性系统分别用状态方程及传递函数来描述。
A simple neural network structure and its learning algorithm are presented for the purpose of system identification of a linear time invariant system which is represented by the state space equation.
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