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.

     

基于Kriging模型和改进MCMC算法的随机有限元模型修正

针对待修正参数维数较高时,标准马尔可夫链蒙特卡罗MCMC (Markov Chain Monte Carlo)算法不易收敛、拒绝率高的问题,提出了基于Kriging模型和在MCMC中融合花朵授粉算法的修正方法.首先,以待修正参数作为输入,以应变模态作为输出,建立Kriging模型,通过蝙蝠算法确定Kriging模型的相关系数;然后,采用最大熵的贝叶斯方法估计参数的后验概率密度函数,将花朵授粉算法融入MH (M etropolis-Hasting)抽样算法,提高局部寻优和全局寻优能力;最后,通过三自由度弹簧-质量系统和三维桁架结构的数值算例验证所提模型修正方法,修正后参数相对误差均低于0.86％.结果 表明,所提方法修正后较高维参数的马尔可夫链能够快速收敛且样本接受率也有所提高,该方法也对随机噪声具有一定的鲁棒性.     

漂浮基柔性关节空间机器人自适应滑模运动控制及振动主动抑制

讨论了关节柔性且系统参数不确定的漂浮基空间机器人系统的动力学建模过程、运动轨迹跟踪控制算法设计及系统柔性振动的主动抑制问题。利用系统动量、动量矩守恒关系和拉格朗日法对系统动力学进行分析,并建立系统动力学方程。基于奇异摄动法将系统分解为表示系统刚性运动部分的慢变子系统和表示系统柔性运动部分的快变子系统。针对慢变子系统提出了一种自适应滑模控制算法。该控制算法是由基于滑模面的等效控制项、自适应控制项和PID反馈控制项组成。因此,它集合了滑模控制、自适应算法和PID技术的优点,且弥补了三种算法各自的缺点。该控制算法能够有效地补偿系统的转动误差和不确定参数,提高控制系统的精度。针对快变子系统,提出基于速度差值的反馈控制算法来抑制柔性关节引起的系统柔性振动,保证系统的稳定性。最后,通过仿真实验证明了提出的混合控制算法的有效性。     

Model-free chaos control based on AHGSA for a vibro-impact system

For a vibro-impact system with clearance, the model-free chaos control method based on adaptive hybrid gravitational search algorithm (or AHGSA algorithm for short) is proposed. Nonparametric time-varying dynamic linear model based on pseudo-partial-derivative is established using input/output data of the controlled system, and on this basis, the optimal controller is designed according to the quadratic performance index, and the controller parameters is optimized using AHGSA algorithm. By combining the artificial bee colony search operator and chaos optimization strategy, gravitational search algorithm (or GSA algorithm for short) is improved from three aspects (i.e., population initialization, velocity and position update, gravity coefficient adjustment) to achieve a balance between the global detection ability and the local development ability. AHGSA algorithm has good optimization accuracy and efficiency: The arbitrariness is avoided in controller parameters selection, and the quality of the chaos control is ensured as well. In simulation experiment, the model-free controller optimized is used to control the chaotic motion of a single-degree-of-freedom vibro-impact system with clearance to verify the validity and feasibility of the proposed chaos control method. The simulation results show that the control effect is good, and the proposed chaos control method has the following advantages: the proposed chaos control method does not depend on the precise model of the controlled system, and the controller is easy to be designed and implemented.     

基于交互式多模型秩滤波的移动机器人组合导航算法

针对非结构化环境下移动机器人组合导航系统中存在的时变或非高斯噪声,将秩滤波器(rank Kalman filter,RKF)与交互式多模型算法(interactive multiple model filter,IMM)相结合,提出一种交互式多模型秩滤波算法(IMM-RKF)。秩滤波根据秩统计量相关原理确定采样点和权值,可适用于具有非高斯噪声的非线性系统;交互式多模型算法是解决结构和参数易发生变化系统中状态估计问题的重要途径,能够抑制组合导航系统中时变噪声引起的导航参数估计误差。仿真实验表明,相比于交互式多模型扩展卡尔曼滤波(IMM-EKF)和交互式多模型无迹卡尔曼滤波(IMM-UKF),提出的IMM-RKF算法能够提高组合导航系统姿态、速度和位置估计精度。     

基于遗传算法的爆炸冲击荷载参数识别方法

基于改进的遗传算法 ,建立了根据测试系统动力响应观测数据反演爆炸冲击荷载参数的数值方法。遗传算法为解决反问题的不适定性提供了强有力的手段。数值模拟结果表明 ,所提出的爆炸冲击荷载参数随机反演方法具有全局搜索能力 ,并且具有良好的抗观测噪音能力。当测试系统的观测相对误差为 10 %时 ,参数反演结果的误差小于 8% ,所建立的参数反演方法具有良好的鲁棒性。     

Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm

This paper is concerned with the parameter estimation of nonlinear chaotic system, which could be essentially formulated as a multi-dimensional optimization problem. In this paper, a hybrid algorithm by combining differential evolution with artificial bee colony is implemented to solve parameter estimation for chaotic systems. Hybrid algorithm combines the exploration of differential evolution with the exploitation of the artificial bee colony effectively. Experiments have been conducted on Lorenz system and Chen system. The proposed algorithm is applied to estimate the parameters of two chaotic systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to differential evolution, artificial bee colony, particle swarm optimization, and genetic algorithm from literature when considering the quality of the solutions obtained.     

基于状态观测器的带分布参数体系地震响应控制

针对带分布参数体系地震响应控制问题,建立带分布参数体系的运动控制方程,推导出应用于带分布参数体系的序列最优模态控制算法。根据序列最优模态控制算法振动控制时对结构全状态反馈的需要,提出了针对序列最优模态控制算法的全维状态观测器,形成基于全维状态观测器的序列最优模态控制算法,然后通过测量容易测量的结构地震响应,由全维状态观...     

Random drift estimation for laser gyroscopes using filters based on the error equations of inertial navigation systems

An algorithm based on the error equations of inertial navigation systems is proposed to estimate the random components of errors for laser gyroscopes. The estimation is performed using the navigation parameters of an inertial system or using the output signals of laser gyroscopes. In the second case, the proposed algorithm is a low-pass filter. Its frequency analysis is compared with other filters.     

基于改进萤火虫算法的移动车辆参数识

提出了一种基于萤火虫优化算法的移动车辆参数直接识别方法.采用四自由度双轴十二参数车辆模型和欧拉梁有限元模型建立了车桥耦合系统的动力方程,并利用Newmark直接积分法求解了系统的动力响应.通过引入一种局部搜索策略和末位淘汰机制改进了萤火虫优化算法的收敛速率,并提高了识别结果的精度.本文方法仅利用车辆振动的竖向加速度响应测量就能进行移动车辆参数的识别.数值算例表明,改进的萤火虫优化算法可以准确地识别出车辆的质量、悬挂刚度和阻尼等参数,并且对测量噪声不敏感.     

A “telescopic” system in the calibration problem for strapdown inertial navigation systems

An algorithm for the bench calibration of strapdown inertial navigation systems is studied. This algorithm was developed at the Moscow Institute of Electromechanics and Automatics. A mathematical model for the calibration process is constructed. A method for representing the original calibration problem in the form of a standard estimation problem is proposed with the aid of a so-called “telescopic” system. On the basis of the original algorithm, a new calibration algorithm is constructed; this new algorithm allows one to improve the estimation accuracy for the parameters of accelerometer and gyroscope units. The maximum attainable estimation accuracy for these parameters is determined.     

Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle

This paper considers iterative identification problems for a Hammerstein nonlinear system which consists of a memoryless nonlinear block followed by a linear dynamical block. The difficulty of identification is that the Hammerstein nonlinear system contains the products of the parameters of the nonlinear part and the linear part, which leads to the unidentifiability of the parameters. In order to obtain unique parameter estimates, we express the output of the system as a linear combination of all the system parameters by means of the key-term separation principle and derive a gradient based iterative identification algorithm by replacing the unknown variables in the information vectors with their estimates. The simulation results indicate that the proposed algorithm can work well.     

部分输入未知条件下结构动力复合反演的分解算法

在充分利用部分输入已确知而部分输入未知的激励特性的基础上，提出了结构动力复合反演的分解算法，该算法从源头上消除了迭代过程中参数识别与荷载反演的相互影响，降低了问题的计算规模。对于线性参数系统，该算法不经过任何迭代计算即可一次性完成结构参数识别及荷载反演。将其与松弛法结合，可解决非线性参数系统的识别问题，与文献[4]的方法比较，其收敛速度有显著提高。     

Identification for disturbed MIMO Wiener systems

The identification of Multi-input Multi-output (MIMO) Wiener systems is concerned in this paper. The system presented is comprised of a multi-dimensional linear subsystem and a memory-less nonlinear block which is made of discontinuous asymmetric piece-wise linear functions. A recursive algorithm is proposed to estimate all the unknown parameters of the system with interference noises. It is shown that the recursive algorithm for the disturbed MIMO Wiener system is convergent. Finally, some simulation results illustrate the identification accuracy and the convergence rate.     

Online system identification using fractional-order Hammerstein model with noise cancellation

Slow convergence and low accuracy are two main drawbacks in nonlinear system identification methods. It becomes more complicated when time delay and noises are considered. In this paper, considering a fractional-order Hammerstein model, an online identification method is proposed. A combination of an evolutionary optimization method and recursive least square algorithm is used to estimate the system parameters and orders in the presence of unknown noises. Finally, simulation results are taken to prove the effectiveness of the proposed algorithm.

     

基于改进无迹卡尔曼滤波器算法的滞回模型参数识别研究

地震产生的周期荷载作用下,钢混桥墩结构表现出滞回行为。为描述滞回行为,研究者提出各类滞回模型,其中BWBN(Bouc-Wen-Baber-Noori)模型可以描述结构滞回行为的强度退化、刚度退化和捏拢效应等典型特征。此外,无迹卡尔曼滤波器UKF(unscented Kalman filter)算法是识别BWBN模型参数的高效方法,但当参数初始值与真实值的偏差过大及缺乏对系统的整体估计时,UKF算法识别过程受到局限。本文改进生成样本点规则,提出改进UKF算法。数值模拟结果表明,在无噪声条件下,改进UKF算法识别得到的参数估计值与准确值的误差平均为1.51%,最大误差为4%;在2%均方根RMS(root mean square)高斯白噪声条件下,误差平均为5.43%,最大误差为18%;在5%RMS高斯白噪声条件下,误差平均为8.9%,最大误差为26%和22%。改进UKF算法识别非线性滞回系统状态估计和BWBN模型参数更加准确和稳定。     

基于交互式多模型滤波的船体变形惯性测量方法

基于惯性测量单元的匹配滤波算法是测量船体变形的发展趋势,然而在实际航行中,船体变形模型参数是未知或存在不确定性,模型参数的这一特性对滤波估计结果影响较大。针对此问题,利用"速度+角速度"匹配算法分析了模型参数未知对滤波估计效果的影响,引入交互式多模型卡尔曼滤波方法,利用不同模型参数的似然函数进行概率分配。最后通过仿真对提出的方法进行了验证,结果表明,与传统卡尔曼滤波相比,估计精度提高了5%~10%,收敛时间提高了1倍,动态变形角的收敛时间在10 s以内,静态变形角的收敛时间在5s以内,提高了系统的环境适应性。     

基于广义卡尔曼滤波的桥梁结构物理参数识别

基于广义卡尔曼滤波提出了随机荷载作用下桥梁结构物理参数的识别方法。首先,以荷载为观测对象,推导出基于有限元模型的桥梁结构系统的观测方程,以结构待识别的物理参数为状态向量,建立系统状态方程;然后,对该状态方程和观测方程构成的非线性参数系统应用广义卡尔曼滤波,从而识别出结构的物理参数。对一座简支梁桥和一座三跨连续梁桥在不同工况下的物理参数识别进行了数值仿真,结果表明本文方法能够准确地识别桥梁结构全部刚度参数、质量参数和阻尼参数,且具有很强的抗噪性能,从而验证了本文方法的有效性和鲁棒性,可应用于识别大型桥梁结构的物理参数。     

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.

     

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Design of adaptive infinite impulse response (IIR) filter is the process of utilizing adaptive algorithm to iteratively determine the filter parameters to obtain an optimal model for the unknown plant based on minimizing the error cost function. However, the error cost surface of IIR filter is generally nonlinear, non-differentiable and multimodal. Hence, an efficient global optimization technique is required to minimize the error cost objective. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA) is proposed in this paper for IIR filter design. The proposed HPSO–GSA updates particle positions through obeying the influence of gravity acceleration in GSA and receiving direction of cognitive memory and social sharing information from PSO by means of coevolutionary strategy. The effect of key parameters on the performance of the proposed algorithm is firstly studied, and the proper parameters in HPSO–GSA are established using five benchmark plants along with the same-order model. The simulation studies have been performed for the performance comparison of eight algorithms such as PSO, GSA, QPSO, DPSO, FO-DPSO, GAPSO, PSOGSA and the proposed HPSO–GSA for unknown IIR system identification with the same-order and reduced-order filters. Simulation results show that the proposed algorithm has advantages over PSO, GSA and other PSO-based variants in terms of the convergence speed and the MSE levels.