随机最优控制方法识别动力学系统局部非线性

利用随机动态规划方法可以得到线性二次型高斯问题的最优控制解．基于这一结果与系统辨识问题最优控制解的概念,将动力学系统中局部非线性结构参数的辨识问题转化为求解对应线性系统的最优控制问题,利用线性系统随机最优控制的理论与方法,结合FSM（ForceStateMapping）方法,提出了识别动力学系统中局部非线性回复力类型及结构参数的新方法．所研究系统由大的线性子结构与一个或多个非线性子结构组成,其中线性结构的模型参数已知,待辨识量为局部非线性结构参数．     

An experimental study of nonlinear dynamic system identification

A technique for robust identification of nonlinear dynamic systems is developed and illustrated using both digital simulations and analog experiments. The technique is based on the Minimum Model Error optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature of the current work is the ability to identify nonlinear dynamic systems without prior assumptions regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.     

Identification method for backbone curve of cantilever beam using van der Pol-type self-excited oscillation

This study presents an experimental method for identification of the backbone curves of cantilevers using the nonlinear dynamics of a van der Pol oscillator. The backbone curve characterizes the nonlinear stiffness and nonlinear inertia of the resonator, so it is important to identify this curve experimentally to realize high-sensitivity and high-accuracy sensing resonators. Unlike the conventional method based on the frequency response under external excitation, the proposed method based on self-excited oscillation enables direct backbone curve identification, because the effect of the viscous environment is eliminated under the linear velocity feedback condition. In this research, the method proposed for discrete systems is extended to give an identification method for continuum systems such as cantilever beams. The actuation is given with respect to both the linear and nonlinear feedbacks so that the system behaves as a van der Pol oscillator with a stable steady-state amplitude. By varying the nonlinear feedback gain, we can produce the self-excited oscillation experimentally with various steady-state amplitudes. Then, using the relationship between these steady-state amplitudes and the corresponding experimentally measured response frequencies, we can detect the backbone curve while varying the nonlinear feedback gain. The efficiency of the proposed method is determined by identifying the backbone curves of a macrocantilever with a tip mass and a macrocantilever subjected to atomic forces, which are representative sources of hardening and softening cubic nonlinearities, respectively.

     

MILM hybrid identification method of fractional order neural-fuzzy Hammerstein model

Aiming at the difficult identification of fractional order Hammerstein nonlinear systems, including many identification parameters and coupling variables, unmeasurable intermediate variables, difficulty in estimating the fractional order, and low accuracy of identification algorithms, a multiple innovation Levenberg–Marquardt algorithm (MILM) hybrid identification method based on the fractional order neuro-fuzzy Hammerstein model is proposed. First, a fractional order discrete neuro-fuzzy Hammerstein system model is constructed; secondly, the neuro-fuzzy network structure and network parameters are determined based on fuzzy clustering, and the self-learning clustering algorithm is used to determine the antecedent parameters of the neuro-fuzzy network model; then the multiple innovation principle is combined with the Levenberg–Marquardt algorithm, and the MILM hybrid algorithm is used to estimate the linear module parameters and fractional order. Finally, the academic example of the fractional order Hammerstein nonlinear system and the example of a flexible manipulator are identified to prove the effectiveness of the proposed algorithm.

     

INCREMENTAL HARMONIC BALANCE METHOD FOR AIRFOIL FLUTTER WITH MULTIPLE STRONG NONLINEARITIES

The incremental harmonic balance method was extended to analyze the flutter of systems with multiple structural strong nonlinearities. The strongly nonlinear cubic plunging and pitching stiffness terms were considered in the flutter equations of two-dimensional airfoil. First, the equations were transferred into matrix form, then the vibration process was divided into the persistent incremental processes of vibration moments. And the expression of their solutions could be obtained by using a certain amplitude as control parameter in the harmonic balance process, and then the bifurcation, limit cycle flutter phenomena and the number of harmonic terms were analyzed. Finally, numerical results calculated by the Runge-Kutta method were given to verify the results obtained by the proposed procedure. It has been shown that the incremental harmonic method is effective and precise in the analysis of strongly nonlinear flutter with multiple structural nonlinearities.     

Low-complexity robust tracking of high-order nonlinear systems with application to underactuated mechanical dynamics

This paper presents a low-complexity design approach with predefined transient and steady-state tracking performance for global practical tracking of uncertain high-order nonlinear systems. It is assumed that all nonlinearities and their bounding functions are unknown and the reference signal is time varying. A simple output tracking scheme consisting of nonlinearly transformed errors and positive design parameters is presented in the presence of virtual and actual control variables with high powers where the error transformation technique using time-varying performance functions is employed. Contrary to the existing results using known nonlinear bounding functions of model nonlinearities, the proposed tracking scheme can be implemented without using nonlinear bounding functions (i.e., the feedback domination design), any adaptive and function approximation techniques for estimating unknown nonlinearities. It is shown that the tracking performance of the proposed control system is ensured within preassigned bounds, regardless of high-power virtual and actual control variables. The motion tracking problem of an underactuated unstable mechanical system with unknown model parameters and nonlinearities is considered as a practical application, and simulation results are provided to show the effectiveness of the proposed theoretical result.     

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.     

Event-triggered output-feedback tracking of a class of nonlinear systems with unknown time delays

A single function approximation (SFA) approach for event-triggered output-feedback tracker design is presented for uncertain nonlinear time-delay systems in lower-triangular form. Contrary to the existing event-triggered output-feedback control methods dependent on multiple function approximators in the presence of lower-triangular nonlinearities, the proposed SFA approach provides the following advantages: (i) the simple observer structure independent of function approximators; (ii) one event-triggering condition based on only a tracking error; and (iii) the simple control scheme using one function approximator. Thus, the structural simplicity is allowed for implementing the observer and the event-triggering law in the sensor part and the adaptive tracker in the control part. Under the proposed SFA-based event-triggered control scheme, it is shown that the boundedness of closed-loop signals and the existence of a minimum inter-event time are guaranteed regardless of unknown time-delay nonlinearities and unmeasurable state variables.

     

非线性系统参数辩识的一种频域模型

本文基于对非线性系统的可分离性假设，将非线性弹性力和阻尼力分别分解为物理坐标下各点间相对位移和相对速度的幂级数函数，导出了一般多自由度非线性系统在恒幅激励下的广义频率响应函数与输入输出之间的迭代关系式，提出了非线性系统中基本线性部分的概念，进而了一种在实验条件下的系统物理参数辩识方法。     

Highly computationally efficient parameter estimation algorithms for a class of nonlinear multivariable systems by utilizing the state estimates

This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the considered system is derived. Second, by cutting down the redundant parameter estimates and extracting the unique parameter estimates from the parameter estimation vector in the least-squares identification method, we present an over-parameterization-based partially coupled average recursive extended least-squares parameter estimation algorithm to estimate the parameters. As for the unknown states in the parameter estimation algorithm, a new state estimator is designed to generate the state estimates. Third, in order to improve the computational efficiency of the parameter estimation algorithm, an over-parameterization-based multi-stage partially coupled average recursive extended least-squares algorithm is proposed. Finally, the computational efficiency analysis and the simulation examples are given to verify the effectiveness of the proposed algorithms.

     

基于支持向量机的结构损伤分步识别研究

支持向量机是一种基于统计学习理论的机器学习算法,能够较好地解决小样本的学习问题。本文介绍了支持向量机分类和回归算法,提出了基于支持向量机的结构损伤分步识别方法:以模态频率作为损伤特征,首先根据支持向量机分类算法的概率估计确定可能的损伤位置,重新构造训练样本,然后利用支持向量机回归算法计算损伤位置;最后估计损伤程度。以梁的损伤识别为例进行了验证,结果表明该方法可以提高损伤识别的精度。     

Identification of the nonlinear vibration characteristics in hydropower house using transfer entropy

The nonlinear behavior is the primary attribute of the dynamic system stability. In this study, the time-delayed transfer entropy method is proposed to identify the nonlinear dynamic behavior of hydropower house and civil construction, including the transport directionality of information, cracked damage location, and degree of dynamic nonlinearity. Differing from the objects investigated in currently available literature, large-scale civil engineering structures such as hydropower house are more complex and larger size, which stimulate the demand for special identification techniques. Due to the fact that nonlinearity of hydropower house can be induced by many types of interactions between structure and mechanism, a simple similarity model of a multistory building, including damaged contact nonlinearity is studied first following by a discussion of the method for identifying information transmission directionality of the linear or nonlinear structure. The method for identifying the source and degrees of structural nonlinearity vibration is described. Furthermore, the procedure for identification of nonlinearity dynamic behavior in the hydropower house structure based on transfer entropy is studied based on a prototype field experiment under various load cases. Rather than the traditional linear signal processing tools and identification methods, the advantage of this proposed method is to identify the nonlinearity dynamic characteristics of hydropower house structure. This study provides a valuable reference for identifying the damage-induced nonlinearities in civil engineering structures as well as studying the nonlinear dynamic characteristics of hydropower house.     

A new numerical technique for the analysis of parametrically excited nonlinear systems

A new computational scheme using Chebyshev polynomials is proposed for the numerical solution of parametrically excited nonlinear systems. The state vector and the periodic coefficients are expanded in Chebyshev polynomials and an integral equation suitable for a Picard-type iteration is formulated. A Chebyshev collocation is applied to the integral with the nonlinearities reducing the problem to the solution of a set of linear algebraic equations in each iteration. The method is equally applicable for nonlinear systems which are represented in state-space form or by a set of second-order differential equations. The proposed technique is found to duplicate the periodic, multi-periodic and chaotic solutions of a parametrically excited system obtained previously using the conventional numerical integration schemes with comparable CPU times. The technique does not require the inversion of the mass matrix in the case of multi degree-of-freedom systems. The present method is also shown to offer significant computational conveniences over the conventional numerical integration routines when used in a scheme for the direct determination of periodic solutions. Of course, the technique is also applicable to non-parametrically excited nonlinear systems as well.     

A new method for Fourier series expansions: Applications in rotor-seal systems

Complex nonlinearities of rotor-seal systems make it difficult to implement some widely used techniques for nonlinear vibrations. This is partly due to the fact that it is rather cumbersome to expand the nonlinear terms into Fourier series. In this paper, a novel Fourier series expansion method is proposed to circumvent this difficulty. The incremental harmonic balance method is utilized to obtain the solutions of a rotor-seal system, where the complicated nonlinearities are handled by the expansion method. Periodic, double-periodic and triple-periodic solutions are obtained in excellent agreement with numerical results, which shows the validity and efficacy of the proposed solution procedures.     

Hybrid approach for dynamic model identification of an electro-hydraulic parallel platform

In this paper, a hybrid optimization algorithm is proposed to identify the dynamic parameters of a 6-DOF electro-hydraulic parallel platform. The dynamic model of a parallel platform with arbitrary geometry, inertia distribution and frictions is obtained based on a structured Boltzmann–Hamel–d’Alembert formulation, and then the estimation equations are explicitly expressed in terms of a linear form with respect to the identified inertial and the friction coefficients in accordance with a linear friction model. However, when nonlinear friction models are considered, the parameter identification of the electro-hydraulic parallel platform is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and then an effective combination of the particle swarm optimization (PSO) method and the local quasi-Newton method is proposed to solve the identification problem. Experimental identification processes are carried out for the identified parameters, and the identified models are compared by the predicted forces between the LS method and the optimization technique as well as between the linear and nonlinear friction models.     

A modified time domain subspace method for nonlinear identification based on nonlinear separation strategy

Nonlinear factors existing in engineering structures have drawn considerable attention, and nonlinear identification is a competent technique to understand the dynamic characteristics of nonlinear structures. Therefore, in this paper, a novel nonlinear separation subspace identification (NSSI) algorithm based on subspace algorithm and nonlinear separation strategy is proposed to conduct nonlinear parameter identification of nonlinear structures. For the proposed NSSI algorithm, the low-level excitation test is firstly conducted to obtain the transfer matrix in the linear response formula. Then, the obtained transfer matrix is used in the high-level excitation test to calculate the nonlinear response part by the proposed nonlinear separation strategy, and the subspace algorithm is utilized to identify the nonlinear parameter on the modified state-space model including only the nonlinear part. The proposed NSSI algorithm can reduce the coupling error caused by simultaneously processing both the large number part (corresponding to the linear part) and small number part (corresponding to the nonlinear part) in the traditional nonlinear subspace identification (NSI) algorithm. At last, two numerical experiments are given to validate the effectiveness of the developed novel nonlinear identification method. Furthermore, some influence factors are discussed to show the stability of the identification algorithm, and some comparisons between the proposed NSSI method and traditional NSI method are also conducted to demonstrate the advantages of the novel method.     

基于仿真和智能算法骨骼肌超弹性本构参数的反演方法研究

从事高强度的体力工作者经常会发生肌肉软组织的损伤, 因此对骨骼肌的变形特性和应力分布的研究受到了越来越多的重视. 获取正确的本构参数对于生物软组织的力学行为的研究至关重要, 而本构参数的确定本质上是一个逆过程, 具有很大的挑战性. 本文分别采用K近邻(K-nearest neighbor, KNN)模型和支持向量机回归(support vector machine regression, SVR)模型并结合非线性有限元仿真, 提出了两种确定骨骼肌本构参数的反演方法. 首先建立了骨骼肌压缩的有限元模型, 对其压缩条件下的变形特性进行了有限元仿真, 得到了相应的变形特性及应力分布规律, 同时也建立了骨骼肌组织的名义应力和主伸长之间非线性关系的数据集. 其次, 分别利用KNN模型和SVR模型搭建了针对骨骼肌组织进行本构参数反演的机器学习智能算法, 对相应的数据集进行训练, 结合单轴压缩实验的实验数据预测了材料的本构参数. 最后, 对分别基于KNN模型和SVR模型对骨骼肌超弹性本构参数的误差结果进行了分析, 通过引入相关系数$R$和决定系数$R^{2}$对采用两种反演方法的有效性进行数值上的验证. 结果表明, 利用KNN模型和SVR模型结合有限元仿真是确定骨骼肌超弹性本构参数的有效、准确的方法, 该方法也可进一步推广到其他类型的非线性软组织的本构参数反演.      

Data-based Identification of nonlinear restoring force under spatially incomplete excitations with power series polynomial model

One of the present barriers to the realization of structural health monitoring is the lack of efficient and general identification methodologies for dealing with nonlinearity, because a priori knowledge of the nature and mathematical form of the nonlinearities of typical engineering structures are usually unknown. The studies on the identification of restoring force, which can be considered as a direct indicator of the extent of the nonlinearity, have received increasing attention in recent years. In this paper, the nonlinear restoring force (NRF) was estimated by using a power series polynomial, and each coefficient of the polynomial was identified by means of standard least-square techniques. No information about the system was needed, and only the applied excitations and the corresponding response time series were used for the identification. Two different cases, in which the system was under complete and incomplete excitations, were investigated. Moreover, the effect of noise level was also taken into consideration. The feasibility and robustness of the proposed approach were verified via a 2-degree-of-freedom (DOF) lumped-mass numerical model, and experimental tests on a 4-story shear building with magneto-rheological (MR) dampers which served to simulate nonlinear behavior. The results show that the proposed data-based method is capable of identifying the NRF in a chain-like multi-degree-of-freedom engineering structures without any assumptions on the structural parameters, and provides a promising way for damage detection in the presence of structural nonlinearities.     

高维非线性振动系统参数识别

将增量谐波平衡非线性识别推广到高维振动系统, 推导了基于增量谐波平衡的多自由度非线性系统的识别方程. 针对一个两自由度系统进行了数值模拟计算, 讨论了系统在单周期、倍周期和混沌运动状态下的参数识别, 以及噪声对识别结果的影响, 验证了增量谐波平衡非线性识别在多自由度系统中的有效性. 结果表明, 该方法具有较高的计算效率和识别精度, 以及良好的抗噪能力.      

基于L1正则化的随机梁式结构静力损伤识别方法

提出了一种结合摄动法和L1正则化方法的随机梁式结构静力损伤识别方法。考虑初始模型误差和测量误差的影响,建立了关于随机损伤指数的控制方程,并将摄动法和L1正则化方法相结合,对随机损伤指数的控制方程进行求解,进而从概率的角度对结构的损伤进行识别。损伤试验结果表明,和传统的最小二乘求解法相比,本文方法能够更为准确地识别多处局部损伤的位置及大小,对实际结构损伤检测具有较好的参考价值。