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...     

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。     

一种压力传感器动态数字滤波的实现方法

本文运用高斯-牛顿法,根据压力传感器的响应曲线建立了传感器的动态模型.该方法可使拟合结果逼近无偏估计,从而提高拟合的精度.为提高传感器动态特性,采用零极点配置法根据动态模型设计了动态补偿数字滤波器.运用Altera提供的DSP Builder开发工具从Simulink模型自动生成VHDL代码,并在FPGA上实现了3阶IIR的数字滤波器,通过仿真取得了较好的效果.     

压电式动态应变测量系统低频特性的补偿研究

针对采用压电元件的动态应变测量系统的低频失真问题,提出一种采用数字滤波器的低频特性补偿方案。通过动态标定试验,获得了压电应变测量系统的阶跃响应特性,以应变计测量结果为期望输出,以压电式传感器测量结果为补偿滤波器的输入,采用M atlab中的系统辨识工具箱设计了数字补偿滤波器。应用结果表明:该方法可使得测量系统的低频下限由原来的2.2Hz降低到10-3Hz以下,低频响应不足引起的失真得到了很好的恢复。     

一种基于改进遗传RBF神经网络的传感器动态特性补偿算法

为了改善传感器的动态特性,减小系统测量误差,分析了传感器动态性能补偿的基本原理,提出了一种基于改进型遗传算法(IAGA)和RBF神经网络相结合的补偿算法,给出了用IAGA-RBF补偿算法建立的数学模型,并应用到瓦斯传感器的补偿环节.实验证明,该补偿算法具有响应速度快、计算精度高和工作频带宽的特点,多项动态特性指标都得到了较大的改善,能够有效地用于传感器的动态特性补偿.     

冲击波测试系统中压力传感器的实时动态补偿实现

冲击波测试系统在测量爆炸冲击信号时,必须解决因传感器的有效带宽难以满足或覆盖被测信号而引起的动态测试误差的问题.为此,利用激波管来对传感器进行动态标定获取实验样本,采用QR分解和改进粒子群优化(PSO)算法进行逆滤波快速估计动态补偿滤波器的阶数和系数.因数字滤波器的有限字长效应,本文选取合适的系数及输出数据的量化位数,来满足测试系统的稳定性.为实现实时在线修正,设计了以FPGA为控制与处理核心的全并行单反馈动态补偿结构,对系统硬件补偿前后的动态特性进行分析.实验表明该补偿滤波器能够满足实际测试需求,能够显著地提高传感器的动态响应特性.     

基于DSP的电涡流传感器的设计

提出了基于DSP的电涡流传感器的设计，实现了非线性补偿、动态特性补偿、滤波器设计等环节，简化了结构并提高了传感器的性能。     

船用温湿度传感器的温度特性研究

引起温湿度传感器测量误差的主要因素是背景噪声和传感器放大电路温漂,为了降低温湿度传感器的测量误差,通过设置滤波器并采用信号叠加方法抑制背景噪声;采用数据拟合技术确定放大电路温度特性,建立温度补偿模型编写温度校正软件并对放大电路温漂进行补偿。试验结果表明,温度补偿后的温湿度传感器测量精度得到明显提高。     

火焰温度场测试中的传感器动态特性补偿

在进行爆温测试时,爆炸伴随的高温火焰具有变化快、温度高、测试环境恶劣等特点.针对这种特定的测温场合,运用火焰温度源法研究了热电偶传感器在火焰温度场中的动态响应特性.为了减小热电偶的动态测量误差,通过粒子群优化算法(PSO)建立了测试系统的动态补偿滤波器模型.实验表明,这种研究方法有效地改善了热电偶的动态测量误差,对通过爆温测试进行弹丸热毁伤评价有一定的参考价值.     

基于光纤微弯传感器的汽车动态称重系统设计

为解决目前汽车动态称重过程中存在的电磁干扰和精确度低的问题,在分析光纤微弯传感器测量原理的基础上,提出了一种基于光纤微弯传感器的汽车动态称重系统.压力的变化引起传感光纤发生弯曲变形,产生输出损耗,通过测量输出光强的变化实现汽车重量的动态称量;设计相应的光电转换和采样放大电路,并采用小波变换对采样信号进行去噪处理.对光纤传感系统的静态响应特性进行验证表明:在0～3 000 kg的范围内光纤传感系统具有良好的线性响应特性,灵敏度为3.8 mV/kg;动态响应实验表明:当汽车通过速度小于15 km/h时,光纤微弯动态称重系统的测量误差小于5.4%,能够满足动态称重的需要.     

基于非线性自适应IIR滤波器的混沌时间序列辨识方法

提出了一种混沌时间序列的非线性自适应IIR滤波辨识算法,该算法采用非线性IIR滤波器来自适应跟踪非线性混沌动力系统的动态特性进行辨识,实验结果表明,该算法具有较高的辨识能力和抗噪声性能。     

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     

AN OPTIMAL DESIGN OF IIR DIGITAL FILTER USING PARTICLE SWARM OPTIMIZATION

In this article, a novel approach for infinite-impulse response (IIR) digital filters using particle swarm optimization (PSO) is presented. IIR filter is essentially a digital filter with recursive responses. Because the error surface of digital IIR filters is generally nonlinear and multimodal, so global optimization techniques are required in order to avoid local minima. This study is based on a heuristic way to design IIR filters. PSO is a powerful global optimization algorithm introduced in combinatorial optimization problems. This study finds the optimum coefficients of the IIR digital filter through PSO. It is found that the calculated values are more optimal than the FDA tool and GA available for the design of the filter in MATLAB. Design of low-pass and high-pass IIR digital filters is proposed in order to provide an estimate of the transition band. The simulation results of the employed examples show an improvement on the transition band. The stability of designed filters is described by the position of Pole-Zeros.     

Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven.     

Characterization of best Chebychev approximation using the frequency response of IIR digital filters with convex stability

This paper deals with the application of Chebychevʼs approximation theory to IIR digital filter frequency response (FR) approximation. It explores the properties of the frequency response of IIR digital filters as a nonlinear complex approximating function; IIR digital filter frequency response is used to approximate a prescribed magnitude and phase responses. The approximation problem is closely related to optimization. If the set of approximating functions is non-convex, the optimization problem is difficult and may converge to a local minimum. The main results presented in the paper are proposing a convex stability domain by introducing a condition termed “sign condition” and characterization of the best approximation by the Global Kolmogorovʼs Criterion (GKC). The Global Kolmogorovʼs Criteria is shown to be also a necessary condition for the approximation problem. Finally, it is proved that the best approximation is a global minimum. The sign condition can be incorporated as a constraint in an optimization algorithm.     

Neural FIR adaptive Laguerre equalizer with a gradient adaptive amplitude for nonlinear channel in communication systems

To mitigate the linear and nonlinear distortions in communication systems, two novel nonlinear adaptive equalizers are proposed on the basis of the neural finite impulse response (FIR) filter, decision feedback architecture and the characteristic of the Laguerre filter. They are neural FIR adaptive decision feedback equalizer (SNNDFE) and neural FIR adaptive Laguerre equalizer (LSNN). Of these two equalizers, the latter is simple and with characteristics of both infinite impulse response (IIR) and FIR filte...     

Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network

This work presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.     

一类不确定非线性系统的自适应鲁棒控制

针对一类由非线性微分方程描述的不确定单输入单输出(SISO)系统,提出了一种自适应鲁棒控制算法.设计了一动态滤波器并将其与原系统组成扩展系统,由滤波器求得控制律稳定扩展系统.算法绕开辨识模型参数而是直接估计函数值,加快了控制律的求解.设计了状态误差观测器,用观测器的值构造控制律逼近状态反馈时的控制效果.基于Lyapunov 稳定理论分析了扩展系统的渐近稳定性并给出了闭环稳定的充分条件.最后仿真结果验证了算法的有效性和鲁棒性.     

Integral variable structure control of nonlinear system using a CMAC neural network learning approach.

This work presents a novel integral variable structure control (IVSC) that combines a cerebellar model articulation controller (CMAC) neural network and a soft supervisor controller for use in designing single-input single-output (SISO) nonlinear system. Based on the Lyapunov theorem, the soft supervisor controller is designed to guarantee the global stability of the system. The CMAC neural network is used to perform the equivalent control on IVSC, using a real-time learning algorithm. The proposed IVSC control scheme alleviates the dependency on system parameters and eliminates the chattering of the control signal through an efficient learning scheme. The CMAC-based IVSC (CIVSC) scheme is proven to be globally stable inasmuch all signals involved are bounded and the tracking error converges to zero. A numerical simulation demonstrates the effectiveness and robustness of the proposed controller.