非线性随机动力学与控制的哈密顿理论框架

 近几年来，笔者提出与发展了随机激励的耗散的哈密顿系统理 论，包括精确平稳解、等效非线性系统法、拟哈密顿系统随机平均法、 拟哈密顿系统的随机稳定性与随机分岔、首次穿越损坏分析方法及非 线性随机最优控制策略，从而构成了一个非线性随机动力学与控制的 哈密顿理论框架.本文简要介绍这一理论框架.     

基于FPK方程的减摆器的非线性阻尼测试方法

受高斯白噪声外激的一阶非线性动力学方程能通过求解对应的FPK方程得到精确稳态解．本文基于这一结果导出减摆器非线性阻尼力与系统速度输出的概率结构的关系,将动力学系统中非线性阻尼力参数的测试问题转化测量系统的概率结构,并通过仿真进行了验证．     

从非线性动力学的视角认识细长压杆的稳定性

工程上大部分机构和结构都处于动载的作用下，受压细长杆的失稳是复杂的动力破坏事件．应用Lagrange描述法建立了两端角铰支受压细长杆的非线性动力学模型，通过对这种模型简化分别得到非线性静力学模型、线性动力学模型和含三次非线性项的动力学模型．利用谱截断方法，讨论了线性动力学模型的局部分岔．通过讨论平衡态存在性和稳定性，得到了含三次非线性项的动力学模型分岔条件．研究表明，受压细长杆的非线性动力学模型中存在叉形分岔．     

复杂机构的控制与结构同步优化设计

本文研究了非线性机构控制与结构同步优化设计问题。提出了一种复杂系统敏度分析的数值求解方法,该方法首先通过滑模控制器将非线性动力学方程化简为一个线性微分方程;然后,利用Newmark积分法,获得系统对设计变量敏度的数值解;最后,以Stewart平台为例,介绍了该方法的应用过程。数值结果说明了方法的有效性。     

索-梁耦合系统解的稳定性分析

研究了在惯性参考系中弹性斜拉索与悬臂梁耦合结构的非线性动力学问题,利用Hamilton原理建立了索-梁耦合系统的非线性动力学方程,利用Galerkin方法将索-梁耦合系统的非线性运动偏微分方程离散为一组常微分方程,然后利用多尺度法分析研究索-梁耦合动力学系统的非线性振动,对耦合系统解的稳定性进行了分析,用Runge-Kutta法对数学模型进行数值计算,并提出对工程有实际意义的结论.     

含J2摄动的有限时间航天器追逃博弈问题研究

航天器追逃博弈是航天器在轨捕获任务的一个重要问题,具有极高的军民两用双重价值．针对有限时间且考虑J2摄动的航天器追逃博弈问题,本文提出了一种精确的求解方法．该方法的核心思想是将有限时间的航天器追逃博弈问题建模为有限时间二人零和对策,则博弈中两航天器的最优控制策略可以转化为有限时间二人零和对策的鞍点解．在鞍点解的求解过程中,本文首先基于考虑J2摄动的非线性动力学方程,将两航天器动力学方程和始末边值条件与鞍点解必要条件结合得到两点边值问题,然后提出一种结合遗传算法和配点法的混合算法求解该两点边值问题以得到精确的鞍点解．本文利用数值仿真对所提方法的有效性进行了验证．结果表明：(i) 在航天器追逃博弈过程中,J2摄动对两航天器的最优控制策略具有较大影响;(ii) 所提方法能够精确求解出两航天器在有限时间的追逃博弈过程中的最优控制策略．     

考虑关节摩擦的空间机器人动力学建模与参数辨识

研究了考虑关节摩擦影响的空间机器人系统的动力学建模与参数辨识问题．采用单向递推组集方法和虚功率原理建立了含有关节摩擦的多体系统动力学方程,推导了关节摩擦对系统动力学方程的贡献,采用基于腕力传感器信号和最小二乘法的辨识方法进行了系统惯性参数的辨识．数值仿真结果验证了数学模型的正确性与辨识方法的有效性．     

连结子结构与自由界面模态综合法在非线性动力分析中的应用

本文提出了一种由线性连接元和非线性连接元组成的连接子结构，并将这种连接子结构用于自由界面的模态综合技术。利用非线性振动理论的渐近方法，求得经模态综合法降维后系统方程的近似解析解。从而将具有连接子结构的自由界面的模态综合技术推广应用到具有局部非线性的复杂结构系统的动力分析，为利用非线性振动理论的渐近方法及动力系统理论进一步研究高维非线性动力学系统的振动特性、分岔及混沌行为创造了一种新的途径。算例表明，该方法具有足够的精度。     

含随机参数的多体系统动力学分析

基于Lagrange方程建立了含随机参数的多体系统的动力学 模型,利用广义坐标分离法将随机微分代数方程转化为随机纯微分方程,利用Newmark法进行数值解算. 应用随机因子法求解系 统随机响应的数字特征,获得统计意义下的解. 以旋转杆滑块系统为例,考虑系统中载荷、物理和几何参数的随机性,通过与Monte Carlo法结果的对比验证了文中方法的正确性和有效性. 计算结果表明,部分随机参数的分散性对多体系统动力响应的影响不可忽略,利用随机参数的动力学模型将能客观地反映出系统的动力学行为.     

拟哈密顿系统非线性随机最优控制

主要介绍近十几年来拟哈密顿系统非线性随机最优控制理论方法及其应用的研究成果, 包括基于拟哈密顿系统随机平均法与随机动态规划原理的非线性随机最优控制基本策略, 即响应极小化控制、随机稳定化、首次穿越损坏最小化控制、以概率密度为目标的控制, 为将它们应用于工程实际而作的部分可观测系统最优控制、有界控制、时滞控制、半主动控制、极小极大控制的进一步研究, 以及综合考虑这些实际问题的非线性随机最优控制的综合策略, 非线性随机最优控制在滞迟系统、分数维系统等中的若干应用, 介绍与这些研究有关的背景, 并指出今后有待进一步研究的问题.     

THEORY AND ALGORITHM OF OPTIMAL CONTROL SOLUTION TO DYNAMIC SYSTEM PARAMETERS IDENTIFICATION (Ⅱ) ──STOCHASTIC SYSTEM PARAMETERS IDENTIFICATION AND APPLICATION EXAMPLE

Based on the contents Of part (Ⅰ) and stochastic optimal control theory, the concept of optimal control solution to parameters identification of stochastic dynamic system is discussed at first. For the completeness of the theory developed in this paper and part (Ⅰ), then the procedure of establishing HamiltonJacobi-Bellman (HJB) equations of parameters identification problem is presented.And then, parameters identification algorithm of stochastic dynamic system is introduced. At last, an application example-local nonlinear parameters identification of dynamic system is presented.     

Optimal bounded control for nonlinear stochastic smart structure systems based on extended Kalman filter

An optimal bounded control strategy for smart structure systems as controlled Hamiltonian systems with random excitations and noised observations is proposed. The basic dynamic equations for a smart structure system with smart sensors and actuators are firstly given. The nonlinear stochastic control system with noised observations is then obtained from the simplified smart structure system, and the system is expressed by generalized Hamiltonian equations with control, random excitation and dissipative forces. The optimal control problem for nonlinear stochastic systems with noised observations includes two parts: optimal state estimation and optimal response control based on estimated states, which are coupled each other. The probability density of optimally estimated systems has generally infinite dimensions based on the separation theorem. The proposed optimal control strategy gives an approximate separate solution. First, the optimally estimated system state is determined by the observations based on the extended Kalman filter, and the estimated nonlinear system with controls and stochastic excitations is obtained which has finite-dimensional probability density. Second, the dynamical programming equation for the estimated system is determined based on the stochastic dynamical programming principle. The control boundedness due to actuator saturation is considered, and the optimal bounded control law is obtained by the programming equation with the bounded control constraint. The optimal control depends on the estimated system state which is determined by noised observations. The proposed optimal bounded control strategy is finally applied to a single-degree-of-freedom nonlinear stochastic system with control and noised observation. The remarkable vibration control effectiveness is illustrated with numerical results. Thus the proposed optimal bounded control strategy is promising for application to nonlinear stochastic smart structure systems with noised observations.     

A nonlinear subspace-prediction error method for identification of nonlinear vibrating structures

The industrial structural systems always contain various kinds of nonlinear factors. Recently, a number of new approaches have been proposed to identify those nonlinear structures. One of the promising methods is the nonlinear subspace identification method (NSIM). The NSIM is derived from the principals of the stochastic subspace identification method (SSIM) and the internal feedback formulation. First, the nonlinearities in the system are regarded as internal feedback forces to its underlying linear dynamic system. The linear and nonlinear components of the identified system can be decoupled. Second, the SSIM is employed to identify the nonlinear coefficients and the frequency response functions of the underlying linear system. A typical SSIM always consists of two steps. The first step makes a projection of certain subspaces generated from the data to identify the extended observability matrix. The second one is to estimate the system matrices from the identified observability matrix. Since the calculated process of the NSIM is non-iterative and this method poses no additional problems on the part of parameterization, the NSIM becomes a promising approach to identify nonlinear structural systems. However, the result generated by the NSIM has its deficiency. One of the drawbacks is that the identified results calculated by the NSIM are not the optimal solutions which reduce the identified accuracy. In this study, a new time-domain subspace method, namely the nonlinear subspace-prediction error method (NSPEM), is proposed to improve the identified accuracy of nonlinear systems. In the improved version of the NSIM, the prediction error method (PEM) is used to reestimate those estimated coefficient matrices of the state-space model after the application of NSIM. With the help of the PEM, the identified results obtained by the NSPEM can truly become the optimal solution in the least square sense. Two numerical examples with local nonlinearities are provided to illustrate the effectiveness and accuracy of the proposed algorithm, showing advantages with respect to the NSIM in a noise environment.     

THEORY AND METHOD OF OPTIMAL CONTROL SOLUTION TODYNAMIC SYSTEM PARAMETERS IDENTIFICATION (Ⅰ)──FUNDAMENTAL CONCEPT AND DETERMINISTIC SYSTEM PARAMETERS IDENTIFICATION

IntroductionDynamicsystemidentificationistheinverseproblemofdynamics.Throughtheuseofexperimentaloroperahonalinput-outputdata,modelofdynaITilcsystemcanbeestablishedbysystemidentificationtechnique,andundetCndnedparametersofmodelcanalsobeidenhfied.Ingeneral,dynamicequahonsofsystemareknownPrior,whilesystemidentificahonisjustanundetendnedparametersidentificationproblem.TheseparametersaremodalparameterssuchasfrequenciesandmodeshapesorstrUctUralparameterssuchasdampingandstiffness.T'hisisatypical"g…     

Parameter identification of systems with multiple disproportional local nonlinearities

Identification of nonlinear systems, especially with multiple local nonlinearities exhibiting disproportional ratios of the degree of nonlinearity and present at a single or multiple spatial locations, is a highly challenging inverse problem. Identification of such complex nonlinear systems cannot be handled easily by the existing conventional restoring force or describing function methods. Further, noise-corrupted measured time history responses make the parameter identification process much more difficult. Keeping this in view, we propose a new meta support vector machine (meta-SVM) model to precisely identify the type, spatial location(s) and also the nonlinear parameters present in disproportionate levels using the noisy measurements. Apart from the conventional SVM model, we also explore the effectiveness of the non-batch processing models like incremental learning for lesser computational cost and increased efficiency. Both incremental and conventional support vector regression models are explored to precisely identify the nonlinear parameters. A numerically simulated multi-degree of freedom spring-mass system with limited multiple local nonlinearities at a few selected spatial locations is considered to illustrate the proposed meta-SVM model for nonlinear parametric identification. However, the extension of the proposed meta-SVM model is rather straightforward to include all types of nonlinearities and cases with the simultaneous existence of multiple numbers of same or different nonlinearities (i.e. combined nonlinearities) at single or multiple locations. It is also clearly established from the numerical simulation studies that the proposed incremental meta-SVM model paves way for online real-time identification of nonlinear parameters which is not yet been addressed in the existing literature.

     

THEORY AND METHOD OF OPTIMAL CONTROL SOLUTION TO DYNAMIC SYSTEM PARAMETERS IDENTIFICATION (I)──FUNDAMENTAL CONCEPT AND DETERMINISTIC SYSTEM PARAMETERS IDENTIFICATION

Based on the concept of optimal control solution to dynamic system parameters identification and the optimal control theory of deterministic system, dynamics system parameters identification problem is brought into correspondence with optimal control problem. Then the theory and algorithm of optimal control are introduced into the study of dynamic system parameters identification. According to the theory of Hamilton-Jacobi-Bellman (HJB) equations' solution, the existence and uniqueness of optimal control solution to dynamic system parameters identification are resolved in this paper. At last the parameters identification algorithm of determi-nistic dynamic system is presented also based on above mentioned theory and concept. Project supported by the National, Defence Science and Technology Foundation (A966000-50) and the Across Century Scientist Foundation from the State Education Commission of China     

Nonlinear normal modes in multi-mode models of an inertially coupled elastic structure

Nonlinear normal modes for elastic structures have been studied extensively in the literature. Most studies have been limited to small nonlinear motions and to structures with geometric nonlinearities. This work investigates the nonlinear normal modes in elastic structures that contain essential inertial nonlinearities. For such structures, based on the works of Crespo da Silva and Meirovitch, a general methodology is developed for obtaining multi-degree-of-freedom discretized models for structures in planar motion. The motion of each substructure is represented by a finite number of substructure admissible functions in a way that the geometric compatibility conditions are automatically assured. The multi degree-of-freedom reduced-order models capture the essential dynamics of the system and also retain explicit dependence on important physical parameters such that parametric studies can be conducted. The specific structure considered is a 3-beam elastic structure with a tip mass. Internal resonance conditions between different linear modes of the structure are identified. For the case of 1:2 internal resonance between two global modes of the structure, a two-mode nonlinear model is then developed and nonlinear normal modes for the structure are studied by the method of multiple time scales as well as by a numerical shooting technique. Bifurcations in the nonlinear normal modes are shown to arise as a function of the internal mistuning that represents variations in the tip mass in the structure. The results of the two techniques are also compared.     

复杂非线性转子—轴承系统动力特性数值分析

研究非线性高维复杂转子－轴承系统的动力特性。针对系统的局部非线性特征,给出了一种降阶及配套动力积分方法。降阶系统仍保持局部非线性特征,非线性响应数值积分所需的迭代只需在局部非线性的维数上执行。对于油膜力无封闭解的实际轴承,采用变分不等方程有限元法求解Reynolds边值问题,使得油膜力及其Jacobian矩阵的计算变得非常简单明了且与具有协调一致的精度。应用上述方法计算分析了一双跨、椭圆轴承－转子系统的不平衡响应,数值结果展现了系统丰富复杂的非线性现象。     

A minimax optimal control strategy for partially observable uncertain quasi-Hamiltonian systems

A stochastic minimax optimal control strategy for partially observable uncertain quasi-Hamiltonian systems is proposed. First, the stochastic optimal control problem of a partially observable nonlinear uncertain quasi-Hamiltonian system is converted into that of a completely observable linear uncertain system based on a theorem due to Charalambous and Elliot. Then, the converted stochastic optimal control problem is solved by a minimax optimal control strategy based on stochastic averaging method and stochastic differential game. The worst-case disturbances and the optimal controls are obtained by solving a Hamilton-Jacobi-Isaacs (HJI) equation. As an example, the stochastic minimax optimal control of a partially observable Duffing–van der Pol oscillator with uncertain disturbances is worked out in detail to illustrate the procedure and effectiveness of the proposed control strategy.     

基于子结构的非线性参数系统动力反演方法研究

研究了输入、输出不完备情况下的非线性参数系统动力反演问题.将子结构技术与分解算法相结合,引入广义逆,无需迭代.直接求得待识别参数的极小范数最小二乘解,反演获得未知输入荷载.本文从理论上论证了该方法的收敛性和严格的适用条件,为有限测点条件下非线性参数系统的动力反演问题提供了一个较好的解决方法.与全量补偿算法相比,计算效率大大提高,具有广泛的工程实际应用前景.数值算例表明该方法具有很好的参数识别精度及荷载反演效果.