振动压路机的一种新模型及其动力学特性研究

本文针对经典的二自由度模型的不足提出了一种具有二级减振系统的三自由度的非线性的振动压路机———土壤系统动力学模型,并模拟实际工况运用计算机仿真技术对该系统进行了动力学仿真和混沌运动的研究,发现了一些对加深业界对振动压路机———土壤系统认识的具有参考价值的新现象。     

外部激励对齿轮箱系统振动能量传递特性的影响

针对齿轮箱的振动噪声控制问题,分析了外部激励对齿轮箱非线性系统振动能量传递特性的影响。采用子结构综合法,建立了齿轮箱非线性耦合系统的动力学模型,计算了耦合系统振动能量传递函数;采用数值求解方法,对齿轮箱非线性系统动力学模型进行求解,建立了系统的状态空间方程,并利用MATLAB对该状态空间方程进行了数值仿真。基于MATLAB,对具有对称结构的齿轮箱系统进行了数值仿真,研究了外部激励对系统振动能量传递特性的影响。该系统为齿轮箱振动噪声控制提供了新的理论依据。     

轴向可伸缩复合材料悬臂梁的非线性振动研究

以航空领域中可变体机翼的伸缩变形过程为研究对象,对可伸缩悬臂复合材料层合梁的时变非线性振动进行理论研究.建立可伸缩悬臂复合材料层合梁在外载荷作用下的非线性动力学模型;根据时变系数非线性动力学方程研究时变非线性振动特性.分析可伸缩悬臂复合材料层合梁在外伸与收缩变形过程中的非线性动力学特性.从数值结果上看:模型的外伸速度、飞行速度对振动的影响较大,初值对振动的影响较小.     

直联式隧道通风机机械传动过程瞬态振动控制

直联式隧道通风机是隧道中的重要设备，其机械传动过程产生的瞬态振动会影响通风机正常运行。为此，该文提出针对直联式隧道通风机机械传动过程瞬态振动的控制方法。首先，通过拉格朗日方程推导出直联式隧道通风机机械传动系统的动力学模型；然后，通过线性FxLMS算法完成对瞬态振动的初步控制；最后，利用跟踪滤波器和非线性变换函数，通过更新准则优化线性FxLMS算法，实现对瞬态振动的精准控制。实验结果表明，应用该方法后，风筒顶层位移和振动加速度幅值明显减小，减振率有所提高。     

电磁振动式微扑翼飞行器动态特性研究

电磁驱动具有驱动电压低、作用力大等优点, 是微机械领域一种重要驱动方式。本文设计了一种电磁振动式微扑翼飞行器，在此基础上，建立了电磁振动式微扑翼飞行器非线性动力学模型，研究了不同电磁力激励下系统动态特性，获得了系统固有频率，并得出方波和正弦半波电磁力驱动能够产生较大扑动幅度的结论。最后，研究了电磁振动式微扑翼飞行器机-磁耦合非线性系统动态特性，研究结果表明电磁振动式微扑翼飞行器适合采用正弦半波电压激励，而且通过结构改进措施，提高了扑动的对称性和稳定性。     

车辆系统的非线性行驶动力学响应

深入研究车辆在粗糙路面行驶而激励引起的非线性振动响应,对揭示复杂动态响应机理和提升动力学性能具有重要的理论意义与工程价值.首先,本文利用运动非线性机制,建立质心垂向平移和绕质心俯仰的两自由度车辆系统的力学模型,利用拉格朗日方程导出系统的非线性运动微分方程,分析自由振动系统的非线性回复力、势能曲线特性及平衡点稳定性.其次,针对自由振动系统,分析线性近似系统的频率比随参数的变化规律,又利用谐波平衡法分析非线性近似系统的幅频曲线特性.最后,针对强迫振动系统,利用数值方法得到系统的阻尼、路面波长及波幅对幅频响应曲线的影响规律.结果表明,新型车辆模型具有复杂非线性动力学特性,为行驶车辆系统提供参数设计和揭示振动机理提供理论参考.     

基于非线性消振器的空间结构被动振动抑制

研究了空间结构振动抑制的被动非线性消振方法.提出了适用于空间环境的非线性消振器结构及动力学模型,该结构通过引入磁力实现空间环境下航天器结构的振动抑制.然后,从理论上建立了含有非线性消振器的空间悬臂梁结构动力学模型,并通过Galerkin截断及数值分析方法分析了瞬态激励下非线性消振器对空间悬臂梁结构的被动振动抑制效果.仿真结果表明,该被动非线性消振器对系统的能量耗散率可以达到92%,可以实现非常好的振动抑制效果,能够适应空间环境,并提高航天系统的可靠性.     

基于非线性自回归时序模型的振动系统辨识

针对线性和弱非线性振动系统进行了研究,提出采用非线性自回归时序(GNAR)模型进行系统频率辨识和判断系统性或非线性基本特征的方法。首先根据摄动法求解非线性微分方程的理论,论证GNAR模型与线性和弱非线性系统之间的本质联系,推导出GNAR模型系数与线性和非线性系统频率之间的解析关系,然后给出由GNAR模型系数和结构判断系统是否存在非线性,及辨识系统频率和非线性项基本特征的方法。最后,以单自由度线性振动系统和无阻尼Duffing振动系统为算例验证该辨识方法的有效性和准确性。实验结果表明,基于GNAR模型的振动系统基本特征辨识方法具有较好的识别精度,能用于估计系统的动力学特性。     

水下涡轮机转子-滚动轴承非线性振动仿真

在考虑支承滚动轴承内部间隙、轴承非线性Hertz接触刚度及转子不平衡量的基础上,建立了水下涡轮机刚性水平转子系统的动力学模型;采用变步长的Rouge-Kutta-Felhberg方法对系统动力学模型进行了数值仿真,基于混沌与分岔理论分析了系统的非线性振动;研究表明,转速较低时,系统的响应以VC周期振动为主;提高转速,系统在旋转频率、VC频率的组合激励下,表现出拟周期振动;继续提高转速时,系统经历阵发性分岔进入混沌状态;研究结论对水下涡轮机系统设计具有重要意义。     

石墨烯树脂复合材料板1∶3内共振非线性动力学分析

石墨烯是一种新型的碳纳米材料,将石墨烯添加到不同基体中可达到增强基体力学性能,并优化结构的效果.本文主要研究了在悬臂边界条件下石墨烯复合材料板的非线性动力学行为,通过Halpin-Tsai模型计算了复合材料板的等效杨氏模量,运用一阶剪切板理论和Hamilton原理得到复合材料板的非线性动力学方程.通过模态缩聚,得到复合材料板横向位移的运动控制方程.应用Rayleigh-Ritz法计算出复合材料板的固有频率,发现第二阶与第四阶固有频率间存在1:3的关系.利用多尺度法研究了复合材料板在1:3内共振情况下的非线性振动响应,通过数值模拟分析了外激励对板结构非线性振动响应的影响.结果表明:横向外激励对复合材料板的非线性动力学行为影响较大,对实际工程具有一定的指导意义.     

非线性系统模型参数估计的算法模型

针对非线性系统模型的多样性,提出了适用于多种非线性模型的基于粒子群优化算法的参数估计方法。计算结果表明,粒子群优化算法是非线性系统模型参数估计的有效工具。     

Vibration control of a nonlinear quarter-car active suspension system by reinforcement learning

This article presents the investigation of performance of a nonlinear quarter-car active suspension system with a stochastic real-valued reinforcement learning control strategy. As an example, a model of a quarter car with a nonlinear suspension spring subjected to excitation from a road profile is considered. The excitation is realised by the roughness of the road. The quarter-car model to be considered here can be approximately described as a nonlinear two degrees of freedom system. The experimental results indicate that the proposed active suspension system suppresses the vibrations greatly. A simulation of a nonlinear quarter-car active suspension system is presented to demonstrate the effectiveness and examine the performance of the learning control algorithm.     

REINFORCEMENT LINEARIZATION CONTROL SYSTEM

The objective of the article is to provide an effective linearization control approach for a nonlinear system. Three reinforcement back propagation learning algorithms (RBPs), based on different step-ahead predictions, are proposed to build the affine linear model of a nonlinear system by means of a composed neural network structure. The approach is used to cancel the effect of nonlinearity of a plant. Reinforcement back propagations can compensate the nonlinearity of the system dynamics between the outputs of the reference model and the system responses. In other words, the role of the composed neural plant is to perform model matching for a linearized system. Based on the derivation of RBPs, a synthetic model, a reinforcement nonlinear control system (RNCS) is developed. This scheme excels the conventional approaches and RBPs. The proposed learning schemes are implemented to linearize a pendulum system. The simulation has been done to illustrate the performance of the proposed learning schemes.     

磁盘读取系统的神经网络控制

该文根据磁盘读取系统的结构,建立了它的物理模型,并指出其存在非线性因素的原因。由于经典控制方法对于非线性系统效果不是很好,因此使用MATLAB神经网络工具箱中的NARMA—L2控制模块进行控制,并利用Simulink可视化建模工具平台设计了整个控制系统。对神经网络控制器进行了训练,并利用经过训练的神经网络控制磁盘读取系统。仿真的结果表明,神经网络NARMA—L2控制模块能够满足含有非线性因素的磁盘读取系统的控制要求。     

Regressor and structure selection in NARX models using a structured ANOVA approach

Regressor selection can be viewed as the first step in the system identification process. The benefits of finding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool analysis of variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model with exogenous input) identification with many candidate regressors.     

Guaranteed cost nonlinear tracking control of a boiler-turbine unit: an LMI approach

This article addresses the problem of designing a guaranteed cost nonlinear state feedback tracking control for a boiler-turbine unit. First, the nonlinear boiler-turbine is re-expressed as a linear system with norm bounded uncertainties via a nonlinear transformation function. Then, based on this linear model a sufficient condition for the existence of a guaranteed cost nonlinear state feedback tracking control is derived in terms of linear matrix inequalities. The advantage of the proposed tracking control design is that only a simple nonlinear controller is constructed and it does not involve feedback linearisation technique and complicated adaptive or fuzzy schemes. An industrial boiler-turbine system is used to illustrate the effectiveness of the proposed design as compared with a linearised approach.     

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real‐time observed data, a cost function with dynamical data is constructed to capture on‐line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.     

Controlling linear systems with nonlinear perturbations under sampled-data output feedback: digital redesign approach

This article develops a digital redesign (DR) technique for sampled-data observer-based output-feedback control of a continuous-time linear system with nonlinear perturbation. It is assumed that the nonlinear perturbation is a locally Lipschitz function. To deal with the discrete-time modelling error in nonlinear systems, as opposed to the previous approach, the DR problem is configured as a stabilisation one for error dynamics between the closed-loop system of nominal linear model under an analogue state-feedback controller and that of the linear system with the nonlinear perturbation under a sampled-data output-feedback controller. A constructive DR condition is formulated in the format of linear matrix inequalities. The stability of the actual sampled-data control system is guaranteed within the DR procedure. The effectiveness of the proposed DR methodology is demonstrated through a numerical simulation.     

An approximated scalar sign function approach to optimal anti-windup digital controller design for continuous-time nonlinear systems with input constraints

This article presents an approximated scalar sign function-based digital design methodology to develop an optimal anti-windup digital controller for analogue nonlinear systems with input constraints. The approximated scalar sign function, a mathematically smooth nonlinear function, is utilised to represent the constrained input functions, which are often expressed by mathematically non-smooth nonlinear functions. Then, an optimal linearisation technique is applied to the resulting nonlinear system (with smooth nonlinear input functions) for finding an optimal linear model, which has the exact dynamics of the original nonlinear system at the operating point of interest. This optimal linear model is used to design an optimal anti-windup LQR, and an iterative procedure is developed to systematically adjust the weighting matrices in the performance index as the actuator saturation occurs. Hence, the designed optimal anti-windup controller would lie within the desired saturation range. In addition, the designed optimal analogue controller is digitally implemented using the prediction-based digital redesign technique for the effective digital control of stable and unstable multivariable nonlinear systems with input constraints.     

Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control

The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle.