A generalized shaping filter method for higher order statistics

The shaping filter method has been a valuable tool for computing the stochastic response of linear dynamical systems to Gaussian stochastic inputs that are not delta-correlated. A straightforward extension of this method to the non-Gaussian case involves determining shaping filters that “match” higher order cumulant functions of the stochastic noise input. However it is easy to find simple examples of input signals for which this will not work because their cumulant functions cannot be realized as cumulant functions of the output of a linear system with delta-correlated input. This paper proposes an alternative shaping filter and it shows that it is applicable to an entire class of input signals for which the original shaping filter method fails.

A further extension of the shaping filter method is suggested, that involves the introduction of the concept of generalized moments that includes as particular cases both the moments and the cumulants.     

Stochastic response moments for linear systems

State space moment analysis is developed as a practical tool for investigating the response of a linear system subjected to stochastic excitation. General formulations are presented to show that the method can be used to evaluate response moments, or cumulants, of any order for both stationary and nonstationary response. The limitation is that the excitation of the linear system must be a generalized white noise called a delta-correlated process. This generalization of the Lyapunov method for finding response covariances gives a comparable matrix method for finding the higher order moments which are often important in predicting failure due to first-passage or fatigue. The technique used here involves rewriting the mth order tensor of mth order cumulants into a minimum length vector, making use of all inherent symmetry, in order to minimize the size of the resulting matrix. Easily implemented algorithms are presented for finding the terms in this matrix. General relationships are also given relating the eigenvalues and eigenvectors of this matrix for mth order cumulants to those of a much smaller matrix. This eigen solution is needed for evaluating nonstationary response cumulants, and the given relationships provide a particularly efficient method for evaluating the eigenvalues. The method is illustrated by evaluating the 35 fourth cumulants of nonstationary response for a class of two-degree-of-freedom oscillators.     

Path integral, functional method, and stochastic dynamical systems

The path-integral approach to dynamical behavior of systems subject to Gaussian white noise is presented in a straightforward manner. Starting from the Chapman-Kolmogorov equation, the transition probability density, and therefore moments and other statistics of the random response are ultimately expressed in terms of functional integrals over the sample-path space. Accordingly, various characteristic functions are replaced by a single generating functional from which moments of all orders are simply calculated through functional differentiation. This generating functional is proven to satisfy a closed system of functional differential equations. These equations are solved in the case of linear systems, their generating functional being obtained in explicit form. Also given in this paper is an integral equation satisfied by the probability densities. Three kinds of approximation method, namely perturbation expansion, Feynman's variational method, and the WKB method, are developed based on the path-integral formalism. They can be used to study the transient as well as stationary behavior of nonlinear systems.     

Non-Gaussian stochastic response of nonlinear compliant platforms

A moment function method is presented to estimate the stochastic response of compliant offshore platforms with nonlinearity in stiffness based on non-Gaussian closure. For guyed towers with clump weight, the nonlinearity in stiffness is of the softening type. The random wave loading is expressed in terms of a rational spectrum, making the system Markovian. Using Ito's rule for stochastic differentiation, differential equations for moments up to the fourth order are developed. The system of equations is closed by considering the fifth and sixth cumulants to be zero. For stationary response, differential equations become algebraic equations. The moments are obtained by solving the system of nonlinear algebraic equations. It is observed that the Gaussian closure method is inadequate for defining the complete probabilistic characteristics of the response.     

Higher order statistics of the response of linear systems excited by polynomials of filtered Poisson pulses

The higher order statistics of the response of linear systems excited by polynomials of filtered Poisson pulses are evaluated by means of knowledge of the first order statistics and without any further integration. This is made possible by a coordinate transformation which replaces the original system by a quasi-linear one with parametric Poisson delta-correlated input; and, for these systems, a simple relationship between first order and higher order statistics is found in which the transition matrix of the dynamical new system, incremented by the correction terms necessary to apply the Itô calculus, appears.     

New partial differential equations governing the joint, response–excitation, probability distributions of nonlinear systems, under general stochastic excitation

In the present work the problem of determining the probabilistic structure of the dynamical response of nonlinear systems subjected to general, external, stochastic excitation is considered. The starting point of our approach is a Hopf-type equation, governing the evolution of the joint, response–excitation, characteristic functional. Exploiting this equation, we derive new linear partial differential equations governing the joint, response–excitation, characteristic (or probability density) function, which can be considered as an extension of the well-known Fokker–Planck–Kolmogorov equation to the case of a general, correlated excitation and, thus, non-Markovian response character. These new equations are supplemented by initial conditions and a marginal compatibility condition (with respect to the known probability distribution of the excitation), which is of non-local character. The validity of this new equation is also checked by showing its equivalence with the infinite system of moment equations. The method is applicable to any differential system, in state-space form, exhibiting polynomial nonlinearities. In this paper the method is illustrated through a detailed analysis of a simple, first-order, scalar equation, with a cubic nonlinearity. It is also shown that various versions of Fokker–Planck–Kolmogorov equation, corresponding to the case of independent-increment excitations, can be derived by using the same approach.

A numerical method for the solution of these new equations is introduced and illustrated through its application to the simple model problem. It is based on the representation of the joint probability density (or characteristic) function by means of a convex superposition of kernel functions, which permits us to satisfy a priori the non-local marginal compatibility condition. On the basis of this representation, the partial differential equation is eventually transformed to a system of ordinary differential equations for the kernel parameters. Extension to general, multidimensional, dynamical systems exhibiting any polynomial nonlinearity will be presented in a forthcoming paper.     

State space analysis of stochastic response cumulants

The technique of linear state space analysis is extended to allow computation of response cumulants of any order. In particular, equations are derived which govern the evolution of the cumulants for the response of a linear system excited by a delta correlated process, which generally causes nonnormal response. The technique also applies if the excitation can be viewed as the output of a linear filter with a delta correlated input. When applied to find second cumulants (i.e., covariances), the method gives the usual Lyapunov matrix equation. The method is illustrated by formulation of the state space equations for the third-order cumulants of the response of an SDF oscillator.     

基于二、四阶累积量的双模式盲均衡算法

针对Shalvi-Weistein(SW)准则和一种有约束条件的SW准则而导出盲均衡算法有不直接性的弊端,介绍了一种归一化累积量的盲均衡准则,同时介绍了基于此准则导出的通过线性时不变系统的盲均衡算法(HOS算法);并将此算法结合传统的直接判决算法,形成了一种基于二阶和四阶累积量的双模式盲均衡(dual-mode HOS)算法。计算机仿真结果表明,双模式盲均衡算法比由准则直接导出的盲均衡算法误差小,有着更优越的性能。     

Response spectral density of linear systems with external and parametric non-Gaussian, delta-correlated excitation

An exact, closed form, analytical expressions for a response spectral density of a certain type of systems, subjected to non-Gaussian, stationary, delta-correlated noise are derived. A new, extended mean square stability conditions are derived for such systems.     

Stabilisation of chaotic dynamics in semiconductor lasers with optical feedback using optimal control

The application of a method for controlling time-delay chaotic systems to the control of external-cavity laser diode dynamics is reported. The approach is based on optimal control theory and, in general, aims to stabilise nonlinear time-delay systems by the use of an approximating sequence technique. The goal of the approximating sequence technique is to reduce a given nonlinear system into a sequence of linear timevarying equations, with the purpose of approximating to the solution of the nonlinear system. In each of these equations, the linear-quadratic optimal tracking control is implemented. It is shown that in the case of chaotic external-cavity lasers, a feedback control via the laser bias current enables the selection of the desired dynamical behaviour.     

Nonlinear random vibration analysis: A Bayesian nonparametric approach

Random vibration analysis aims to estimate the response statistics of dynamical systems subject to stochastic excitations. Stochastic differential equations (SDEs) that govern the response of general nonlinear systems are often complicated, and their analytical solutions are scarce. Thus, a range of approximate methods and simulation techniques have been developed. This paper develops a hybrid approach that approximates the governing SDE of nonlinear systems using a small number of response simulations and information available a priori. The main idea is to identify a set of surrogate linear systems such that their response probability distributions collectively estimate the response probability distribution of the original nonlinear system. To identify the surrogate linear systems, the proposed method integrates the simulated responses of the original nonlinear system with information available a priori about the number and parameters of the surrogate linear systems. There will be epistemic uncertainty in the number and parameters of the surrogate linear systems because of the limited data. This paper proposes a Bayesian nonparametric approach, called a Dirichlet Process Mixture Model, to capture these uncertainties. The Dirichlet process models the uncertainty over an infinite-dimensional parameter space, representing an infinite number of potential surrogate linear systems. Specifically, the proposed method allows the number of surrogate linear systems to grow indefinitely as the nonlinear system observed dynamic unveil new patterns. The quantified uncertainty in the estimates of the unknown model parameters propagates into the response probability distribution. The paper then shows that, under some mild conditions, the estimated probability distribution approaches, as close as desired, to the original nonlinear system’s response probability distribution. As a measure of model accuracy, the paper provides the convergence rate of the response probability distribution. Because the posterior distribution of the unknown model parameters is often not analytically tractable, a Gibbs sampling algorithm is presented to draw samples from the posterior distribution. Variational Bayesian inference is also introduced to derive an approximate closed-form expression for the posterior distribution. The paper illustrates the proposed method through the random vibration analysis of a nonlinear elastic and a nonlinear hysteretic system.     

用局部点振动数据重构系统物理参数

本文提出用系统的局部振动测量来识别其物理参数的一种方法。文章首先运用微分方程的算子解法，将描述结构振动的动力学方程转化为由测量点信号表示的输入输出系统参数模型，并建立该模型参数─—系统参数与物理参数间的对应关系，然后运用系统辨识技术识别出该系统参数，最后利用系统参数与物理参数间的映射关系，反推出待识别系统的物理参数。     

Synthesis of multi-variate Volterra systems by a topological assemblage scheme

The Volterra series expansion is widely employed to represent the input–output relationship of nonlinear dynamical systems. Such a representation is based on the Volterra frequency-response functions (VFRF), which can be calculated from the governing equations of the system by the harmonic probing method. This operation is straightforward for simple systems, but may become very complicated for multi-variate or high-order systems. An alternative technique for the evaluation of the VFRFs of multi-variate systems is presented here generalizing a previously reported technique that focused on uni-variate cases. Additionally, it is extended to derive, within the same framework, a set of associated linear equations (ALE). Examples of a 2-dof system with polynomial nonlinearities, a wind-excited cable and a 2-dof system subjected to ocean waves are utilized to demonstrate applications of the proposed technique.     

非光滑碰撞振动系统动力学分析的Lyapunov指数判据

对n维非光滑(刚性约束和分段光滑)碰撞振动系统引进局部映射,利用Poincaré映射分析方法,建立了该类系统的Lyapunov指数谱与Floquet特征乘子之间的解析关系,提出了非光滑碰撞振动系统动力学分析的Lyapunov指数判据。以一类刚性约束的非线性碰撞振动系统为例,给出该系统的Lyapunov指数谱随参数大范围变化的规律,并将此规律与相应的Poincaré映射分岔图进行仔细对照,得到了一致的结论,验证了上述动力学分析的Lyapunov指数判据的正确性和有效性。     

Response probabilities of nonlinear random systems: a compartmental approach

In this paper, a compartmental approach is applied to the study of response behaviour of nonlinear systems subject to random external excitations and random parametric variations. A distinct advantage is that, while the underlying system is nonlinear, the compartmental equations governing the response probabilities are linear and homogeneous. The dimension of this system of equations, however, is large in general but its solution is facilitated by the fact that its coefficient matrix is sparse. Computational aspects and stability of the compartmental probabilities are studied in detail.     

Stochastic response of linear and non-linear systems to α-stable Lévy white noises

The stochastic response of linear and non-linear systems to external α-stable Lévy white noises is investigated. In the literature, a differential equation in the characteristic function (CF) of the response has been recently derived for scalar systems only, within the theory of the so-called fractional Einstein–Smoluchowsky equations (FESEs). Herein, it is shown that the same equation may be built by rules of stochastic differential calculus, previously applied by one of the authors to systems driven by arbitrary delta-correlated processes. In this context, a straightforward formulation for multi-degree-of-freedom (MDOF) systems is also developed.Approximate CF solutions to the derived equation are sought for polynomial non-linearities, in stationary conditions. To this aim a wavelet representation is used, in conjunction with a weighted residual method. Numerical results prove in excellent agreement with exact solutions, when available, and digital simulation data.     

热冲击下的复合材料壳刚柔耦合动力学研究

研究了在热冲击下任意形状(仅一个方向有曲率)复合材料壳的非线性刚柔耦合动力学响应。根据Mindlin理论,建立了任意形状的复合材料壳的非线性应变-位移关系。借助于数学理论以及几何关系,描述了壳上任意点的变曲率。用虚功原理建立了动力学变分方程,并采用等参单元对壳的连续动力学方程进行离散,建立了中心刚体-复合材料壳的刚-柔耦合动力学方程。用高斯积分计算常值阵,为了提高计算效率,采用广义-α法结合Newton-Raph-son迭代法对动力学方程进行积分。将采用该方法计算得到的频率与ANSYS软件计算得到的作对比,验证了模型的正确性。通过算例分析了在热冲击作用下复合材料壳的线性、非线性的动力学特性,以及曲率、材料特性对动力学响应的影响。     

Optimal bounded control of harmonically and stochastically excited strongly nonlinear oscillators

A procedure for designing optimal bounded control to minimize the response of harmonically and stochastically excited strongly nonlinear oscillators is proposed. First, the stochastic averaging method for controlled strongly nonlinear oscillators under combined harmonic and white noise excitations using generalized harmonic functions is introduced. Then, the dynamical programming equation for the control problem of minimizing response of the systems is formulated from the partially completed averaged Itô equations by using the dynamical programming principle. The optimal control law is derived from the dynamical programming equation and control constraint without solving the dynamical programming equation. Finally, the stationary probability density of the amplitude and mean amplitude of the optimally controlled systems are obtained from solving the reduced Fokker–Planck–Kolmogorov equation associated with fully completed averaged Itô equations. An example is given to illustrate the proposed procedure and the results obtained are verified by using those from digital simulation.     

Ito and Stratonovich integrals for delta-correlated processes

In this paper the generalization of the Itd and Stratonovich integrals for the case of non-linear systems excited by parametric delta-correlated processes is presented. This generalization gives a new light on the corrective coefficients in the stochastic differential equations driven by parametric delta-correlated processes. The full significance of these corrective terms is evidenced by means of some examples.