传感器动态非线性补偿方法及DSP实现

利用Hammerstein模型描述传感器的动态非线性。动态非线性环节表示为静态非线性子环节和动态线性子环节的串联,相应的动态非线性补偿分为两个阶段：动态线性补偿和静态非线性校正。通过仿真和对腕力传感器响应的补偿验证了两阶段补偿方法的可行性。研制了基于DSP的动态非线性实时补偿系统,通过实验验证了动态非线性补偿方法的有效性。     

利用SVR的非线性动态系统Wiener模型补偿

提出一种新的利用支持向量回归机(SVR)的非线性动态系统维纳(Wiener)模型补偿方法.首先,将非线性动态系统用Wiener模型描述成线性动态子环节和非线性静态增益;再设计结构上与之对应的Wiener补偿器,并进一步将其变换为可用SVR辨识的线性中间模型;最后,通过关系矩阵将中间模型的估计值转换为Wiener补偿器的实际参数.用实际压力响应系统的动态标定实验数据进行测试,结果表明,与最小二乘方法比较,所提方法建立的Wiener补偿器具有更强的抗干扰能力.因此,该研究为非线性动态系统补偿又提供了一种可选方法.     

基于SVR的传感器Hammerstein模型辨识

提出一种基于支持向量回归机的非线性动态传感器Hammerstein模型辨识方法并给出了相关的数学理论及学习算法.在该模型中,用非线性静态子环节和线性动态子环节串联来描述传感器的非线性动态特性.再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型,并通过SVR求取中间模型参数.最后,推导出中间模型参数与传感器Hammerstein模型参数之间的关系,并由该关系实现非线性静态环节和线性动态环节的同时辨识.用实际力传感器动态标定实验数据进行测试,结果表明与常规非线性传感器辨识方法不同,所提方法只需进行一次动态标定实验就能给出非线性动态模型的数学解析表达式.且建立的力传感器Hammerstein模型阶次为4,而线性动态系统模型则需要6阶才能达到相同的精度.因此该研究为传感器非线性动态系统辨识又提供了一种可选方法.     

基于Wiener模型的传感器动态非线性辨识研究

针对实际测量中传感器存在较大非线性的缺点,提出利用改进型Wiener模型描述传感器动态非线性模型;将Wiener模型的动态线性环节和静态非线性环节分别利用Laguerre函数和最小二乘支持向量机进行辨识,最终实现传感器模型的建立;通过仿真实验验证比较不同方法的辨识误差与速度,最终结果表明该方法在非线性动态传感器模型辨识方面具有明显的速度和精度优势。     

基于SVR的非线性动态系统建模方法研究

提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。用非线性静态子环节和线性动态子环节串联——Hammerstein模型来描述非线性动态系统。然后,通过函数展开将Hammerstein模型的非线性传递函数转换为等价的线性形式,从而建立起线性中间模型。再由SVR算法辨识出中间模型参数。最后,通过中间模型参数与Hammerstein模型参数之间的关系,实现原系统的非线性静态环节和线性动态环节的同时辨识。用非线性动态系统标定实验数据进行测试,建模结果表明所提方法具有如下优点：1)只需进行一次动态标定实验; 2)能给出非线性动态模型的数学解析表达式;3)充分利用SVR的优点,使所建模型具有更好的鲁棒性。该研究为非线性动态系统建模又提供了一种新方法。     

基于Hammerstein型神经网络的非线性动态系统辨识

Hammerstein模型广泛应用于非线性系统的辨识中,其结构是由非线性静态增益部分和一个线性动态部分串联。提出一种Hammerstein型神经网络用来模拟传统的Hammerstein模型,并将其应用于非线性动态系统的辨识中。由Lipschitz熵来确定Hammerstein型神经网络的阶次,并利用反向传播算法对网络权值的进行训练。仿真结果表明,Hammerstein型神经网络具有较好的非线性动态系统辨识性能。     

非线性动态系统建模方法研究

讨论了一类非线性动态系统建模的新方法。首先,假设原非线性动态系统可以用Hammerstein模型来表示。然后,将Hammerstein模型的非线性传递函数转换为等价的线性形式,从而建立起中间模型。接下来,利用粒子群优化（Particle Swarm Optimization,PSO）算法辨识出中间模型参数。最后,通过中间模型参数与Hammerstein模型参数之间的关系,推出非线性静态环节和线性动态环节的参数,从而实现原非线性动态系统建模。为了进一步增强建模的性能,提出了利用一种改进的粒子群优化（Improved Particle Swarm Optimization,IPSO）算法。仿真结果说明了该方法的合理性和有效性。     

基于逆模型辨识的Wiener型传感器动态补偿研究

在测量系统中许多传感器动态特性是一个非线性Wiener模型,即存在着严重的静态非线性和动态响应滞后.为了补偿动态误差,采用模型参考和Wiener逆模型辨识的算法建立动态补偿单元.补偿单元由一个静态逆模型和动态逆模型构成.通过静态标定方法,采用单输入/单输出的模糊小脑神经网络(SISO-FCMAC)建立传感器静态非线性模...     

非线性动态传感器系统的H模型神经网络辨识

针对非线性动态传感器模型辨识问题,提出一种新的Hammerstein模型神经网络结构辨识法。非线性动态传感器系统采用Hammerstein模型描述,将系统分解为非线性静态增益串接线性动态环节。再设计一种网络权系数对应于相应的Hammerstein模型参数的新型神经网络结构,推导了基于反向传播的网络权系数调整方法。通过网络迭代训练同时得到静态与动态两个环节的模型参数。最后通过一个H模型的数值仿真来验证方法的有效性,仿真结果表明所提辨识方法是有效的。     

非线性动态系统的Wiener神经网络辨识法

提出了一种新的Wiener神经网络结构并将其应用于非线性动态系统辨识问题.首先,用Wiener模型对非线性动态系统进行描述,将其分解成线性动态子环节串接非线性静态增益的形式.其次,设计一种新型的神经网络结构,使网络权值对应于相应的Wiener模型参数;并推导了基于反向传播的网络权值调整方法.最后,通过网络迭代训练,可同时得到线性动态子环节和非线性静态增益的模型参数.通过一个Wiener模型的数值仿真来验证方法的有效性,仿真结果表明所提辨识方法切实可行.     

轧辊偏心的H∞输出反馈鲁棒重复控制

针对轧辊偏心问题,用线性矩阵不等式(LMI)方法设计了用于轧辊偏心补偿控制的H∞输出反馈鲁棒重复控制器,首先引入动态输出反馈来保证闭环系统的鲁棒稳定性,把重复控制器设计问题转化为H∞动态反馈控制器的设计问题,采用变量替换法将非线性矩阵不等式转化为线性矩阵不等式并对其求解进而得到控制器参数.另外在采用上述控制器保证系统鲁棒稳定性的同时,通过在重复控制器中引入一个前向系数进一步改善和提高系统的动态性能与稳态控制精度.理论证明与仿真研究表明当系统对象参数存在摄动时,这种控制器仍能有效地补偿轧辊偏心对产品质量的影响.     

Identification of systems containing linear dynamic and static nonlinear elements

Identification of nonlinear systems which can be represented by combinations of linear dynamic and static nonlinear elements are considered. Previous results by the authors based on correlation analysis are combined to provide a unified treatment for this class of systems. It is shown that systems composed of cascade, feedforward, feedback and multiplicative connections of linear dynamic and zero memory nonlinear elements can be identified in terms of the individual component subsystems from measurements of the system input and output only.     

含有摩擦补偿的全方位移动机器人自抗扰控制

本文针对全方位移动机器人轨迹追踪中的摩擦补偿问题,提出了一种改进的非线性自抗扰控制器.首先建立了含有经典静态摩擦模型的全方位移动机器人动力学模型.其次,基于该模型设计非线性控制器和线性扩张状态观测器并给出了系统的稳定性分析.通过将模型已知项加入线性扩张状态观测器中得到摩擦力的估计值,并将估计值用于非线性控制器中摩擦补偿部分.为减小摩擦力对机器人低速运动轨迹追踪控制的影响,非线性控制器采用变增益控制器进行轨迹追踪控制.最后通过仿真结果验证本文提出控制器的有效性.     

Nonlinear dynamic response structural optimization using equivalent static loads

It is well known that nonlinear dynamic response optimization using a conventional optimization algorithm is fairly difficult and expensive for the gradient or non-gradient based optimization methods because many nonlinear dynamic analyses are required. Therefore, it is quite difficult to find practical large scale examples with many design variables and constraints for nonlinear dynamic response structural optimization. The equivalent static loads (ESLs) method is newly proposed and applied to nonlinear dynamic response optimization. The equivalent static loads are defined as the linear static load sets which generate the same response field in linear static analysis as that from nonlinear dynamic analysis. The ESLs are made from the results of nonlinear dynamic analysis and used as external forces in linear static response optimization. Then the same response from nonlinear dynamic analysis can be considered throughout linear static response optimization. The updated design from linear response optimization is used again in nonlinear dynamic analysis and the process proceeds in a cyclic manner until the convergence criteria are satisfied. Several examples are solved to validate the method. The results are compared to those of the conventional method with sensitivity analysis using the finite difference method.     

Measurement and identification of nonlinear systems consisting oflinear dynamic blocks and one static nonlinearity

This paper considers the measurement and the identification of nonlinear time-invariant single-input/single-output (SISO) systems, consisting of a multivariable linear dynamic system and one static nonlinear SISO system. This includes Wiener-Hammerstein systems in a linear feedback loop. The nonparametric identification of the frequency response functions of the linear parts are obtained without measuring the signals over the static nonlinearity. Measurements on an electronic circuit demonstrate the usability of this identification scheme     

Hammerstein-Wiener system estimator initialization

In nonlinear system identification, the system is often represented as a series of blocks linked together. Such block-oriented models are built with static nonlinear subsystems and linear dynamic systems. This paper deals with the identification of the Hammerstein-Wiener model, which is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output. The proposed identification scheme is iterative and will be demonstrated on measurements. It will be proven that on noiseless data and in absence of modeling errors, the optimization procedure converges to the true system locally.     

Support vector method for identification of Wiener models

Support vector regression is applied to identify nonlinear systems represented by Wiener models, consisting of a linear dynamic system in series with a static nonlinear block. The linear block is expanded in terms of basis functions, such as Laguerre or Kautz filters, and the static nonlinear block is determined using support vector machine regression.