Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems

We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input–output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.     

Least squares algorithm for an input nonlinear system with a dynamic subspace state space model

For a Hammerstein input nonlinear system with a subspace state space linear element, this paper transforms the system into a bilinear identification model by using the property of the shift operator to the state space model and presents a recursive and an iterative least squares algorithms to generate parameter estimates and state estimates by using the hierarchical identification principle and by replacing the unknown state variables with their estimates. The proposed approaches are computationally more efficient than the over-parameterization model based least squares method.     

Gradient-Based Identification Methods for Hammerstein Nonlinear ARMAX Models

Two identification algorithms, an iterative gradient and a recursive stochastic gradient based, are developed for a Hammerstein nonlinear ARMAX model, a linear dynamical block following a memoryless nonlinear block. The basic idea is to use the gradient search principle, to replace unmeasurable noise terms in the information vectors by their estimates, and to compute iteratively or recursively the noise estimates based on the obtained parameter estimates. Convergence analysis of the recursive stochastic gradient algorithm indicates that the parameter estimation error consistently converges to zero under certain conditions. The simulation results show the effectiveness of the proposed algorithms. An erratum to this article is available at .     

Recursive parameter identification of the dynamical models for bilinear state space systems

This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based stochastic gradient (O-SG) algorithm is presented for the bilinear state space systems by using the gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG algorithm, a state observer-based multi-innovation stochastic gradient algorithm and a state observer-based recursive least squares identification algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed algorithms.     

A novel APSO-aided maximum likelihood identification method for Hammerstein systems

Identification of Hammerstein nonlinear models has received much attention due to its ability to describe a wide variety of nonlinear systems. In this paper the maximum likelihood estimator which was originally derived for linear systems is extended to work for Hammerstein nonlinear systems in colored-noise environment. The maximum likelihood estimate is known to be statistically efficient, but can lead to complex nonlinear multidimensional optimization problem; traditional methods solve this problem at the computational cost of evaluating second derivatives. To overcome these shortcomings, a particle swarm optimization (PSO) aided maximum likelihood identification algorithm (Maximum Likelihood-Particle Swarm Optimization, ML-PSO) is first proposed to integrate PSO’s simplicity in implementation and computation, and its ability to quickly converge to a reasonably good solution. Furthermore, a novel adaptive strategy using the evolution state estimation technique is proposed to improve PSO’s performance (maximum likelihood-adaptive particle swarm optimization, ML-APSO). A simulation example shows that ML-APSO method outperforms ML-PSO and traditional recursive least square method in various noise conditions, and thus proves the effectiveness of the proposed identification scheme.     

Long-memory recursive prediction error method for identification of continuous-time fractional models

This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models.

     

Gradient-based parameter estimation for input nonlinear systems with ARMA noises based on the auxiliary model

This paper presents a gradient-based iterative identification algorithm and an auxiliary-model-based multi-innovation generalized extended stochastic gradient algorithm for input nonlinear systems with autoregressive moving average (ARMA) noises, i.e., the input nonlinear Box–Jenkins (IN–BJ) systems. The estimation errors given by the gradient-based iterative algorithm are smaller than the generalized extended stochastic gradient algorithm under same data lengths. A simulation example is provided.     

Restoring force and dynamic loadings identification for a nonlinear chain-like structure with partially unknown excitations

Most of the currently employed vibration-based identification approaches for structural damage detection are based on eigenvalues and/or eigenvectors extracted from dynamic response measurements, and strictly speaking, are only suitable for linear system. However, the inception and growth of damage in engineering structures under severe dynamic loadings are typical nonlinear procedures. Consequently, it is crucial to develop general structural restoring force and excitation identification approaches for nonlinear dynamic systems because the restoring force rather than equivalent stiffness can act as a direct indicator of the extent of the nonlinearity and be used to quantitatively evaluate the absorbed energy during vibration, and the dynamic loading is an important factor for structural remaining life forecast. In this study, based on the instantaneous state vectors and partially unknown excitation, a power series polynomial model (PSPM) was utilized to model the nonlinear restoring force (NRF) of a chain-like nonlinear multi-degree-of-freedom (MDOF) structure. To improve the efficiency and accuracy of the proposed approach, an iterative approach, namely weighted adaptive iterative least-squares estimation with incomplete measured excitations (WAILSE-IME), where a weight coefficient and a learning coefficient were involved, was proposed to identify the restoring force of the structure as well as the unknown dynamic loadings simultaneously. The response measurements of the structure, i.e., the acceleration, velocity, and displacement, and partially known excitations were utilized for identification. The feasibility and robustness of the proposed approach was verified by numerical simulation with a 4 degree-of-freedom (DOF) numerical model incorporating a nonlinear structural member, and by experimental measurements with a four-story frame model equipped with two magneto-rheological (MR) dampers mimicking nonlinear behavior. The results show the proposed approach by combining the PSPM and WAILSE-IME algorithm is capable of effectively representing and identifying the NRF of the chain-like MDOF nonlinear system with partially unknown external excitations, and provide a potential way for damage prognosis and condition evaluation of engineering structures under dynamic loadings which should be regarded as a nonlinear system.     

基于非线性状态空间辨识的气动弹性模型降阶

高维、非线性气动弹性系统的模型降阶是当前气动弹性力学与控制领域的研究热点之一.然而国内外现有的非线性模型降阶方法仍存在辨识算法复杂、精度有待提高等问题.本研究提出了一种基于非线性状态空间辨识的跨音速气动弹性模型降阶方法. 首先,该方法基于非定常空气动力的单位脉冲响应数据,采用特征系统实现算法对非线性状态空间模型的线性动力学部分进行系统辨识. 其次,引入状态和控制输入的非线性函数, 采用优化算法对非线性函数的系数矩阵进行优化,进而得到考虑非线性效应的空气动力降阶模型.为了验证该降阶模型在预测跨音速气动弹性力学行为的精确性,本文以三维机翼为研究对象,分别从基于非线性降阶模型的气动力辨识、跨声速颤振边界计算和极限环振荡预测三方面进行了算例验证,并与现有的模型降阶方法进行了对比, 进一步说明本文所提出方法的有效性.研究结果表明, 该降阶模型对上述三类问题的计算精度与直接流-固耦合方法相吻合,可用于高效预测飞行器跨声速气动弹性力学行为.      

A time-varying forgetting factor stochastic gradient combined with Kalman filter algorithm for parameter identification of dynamic systems

Parameter estimation problem of a class of observer canonical state-space system is considered in this paper. By means of the property of the shift operator, the space-state model is transformed into the input–output representations. Then, a time-varying forgetting factor stochastic gradient combined with Kalman filter algorithm is proposed. The proposed algorithm is based on interactively estimating unknown parameters to achieve all the parameters identification of the system. A numerical example is provided to verify the effectiveness of the proposed algorithm.     

Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle

This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input–output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.     

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.     

Identification of non-linear structural parameters under limited input and output measurements

In this paper, an algorithm is proposed for the identification of non-linear structural parameters under limited input and output measurements. The algorithm is based on the sequential application of extended Kalman estimator for the non-linear structural parameters and the least-squares estimation for the unmeasured excitation. First, the identification of small size non-linear structural parameters is studied. Then, based on the substructure approach, the algorithm is extended to identify large size non-linear structural parameters. Interconnection effect between adjacent substructures is accounted by considering the interconnection forces at substructural interfaces as ‘additional unknown inputs’ to the substructures. The general case that measurements at the substructure interfaces are not available is considered. Two numerical examples which identify non-linear hysteretic parameters of a four-story and an eight-story hysteretic shear-beam building subject to unmeasured excitation respectively, demonstrate the efficiency of the proposed algorithm.     

Newton iterative identification method for an input nonlinear finite impulse response system with moving average noise using the key variables separation technique

This paper studies parameter identification problems for input nonlinear finite impulse response systems with moving average noise (i.e., input nonlinear finite impulse response moving average systems). Since the identification model of the system contains the product of the parameters of the nonlinear part and the linear part, we use the key variables separation technique and express the output of the system as the linear combination of all parameters, and then derive a Newton iterative identification method. The simulation results show that the proposed algorithm is effective.     

Decentralized direct adaptive fuzzy control of robots using voltage control strategy

Decentralized control is the most favorite control of robot manipulators due to computational simplicity and ease of implementation. Beside that, adaptive fuzzy control efficiently controls uncertain nonlinear systems. These motivate us to design a decentralized fuzzy controller. However, there are some challenging problems to guarantee stability. The state-space model of the robotic system including the robot manipulator and motors is in a noncompanion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. For this purpose, adaptive fuzzy control may use all variable states. As a result, it suffers from computational burden. To overcome the problems, we present a novel decentralized Direct Adaptive Fuzzy Control (DAFC) of electrically driven robot manipulators using the voltage control strategy. The proposed DAFC is simple, in a decentralized structure with high-accuracy response, robust tracking performance, and guaranteed stability. Instead of all state variables, only the tracking error of every joint and its derivative are given as the inputs of the controller. The proposed DAFC is simulated on a SCARA robot driven by permanent magnet dc motors. Simulation results verify superiority of the decentralized DAFC to a decentralized PD-fuzzy controller.     

Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems

This paper focuses on the identification problem of Hammerstein systems with dual-rate sampling. Using the key-term separation principle, we derive a regression identification model with different input updating and output sampling rates. To solve the identification problem of the dual-rate Hammerstein systems with the unmeasurable variables in the information vector, an auxiliary model-based recursive least squares algorithm is presented by replacing the unmeasurable variables with their corresponding recursive estimates. Convergence properties of the algorithm are analyzed. Simulation results show that the proposed algorithm can estimate the parameters of a class of nonlinear systems.     

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.     

基于黎曼几何的非线性振子动力学分析递推解析算法

基于黎曼几何和变分原理,推导了黎曼流形上非线性耗散动力系统的二阶微分动力学方程,并运用流形收缩的概念将动力学方程离散化,进而建立了相应的递推求解格式。选取3个自治非线性阻尼振子系统,分别采用递推解析算法和龙格库塔法求解微分动力学方程,并比较分析了不同的时间步长下两种算法的计算耗时。结果表明,与龙格库塔法相比,基于黎曼几何的递推算法不仅能得到每一时步的解析表达式,而且计算耗时短,计算效率高。基于黎曼流形的动力学方程递推算法为非线性动力学系统的解析求解提供了新思路。     

基于混合遗传算法的动力系统阻尼参数识别方法

将动力系统阻尼参数识别反问题转化为非线性优化问题处理，提出了基于遗传算法的动力系统阻尼参数识别方法。为了提高简单遗传算法的计算效率和处理早熟问题，将模拟退火算法与遗传算法相结合，建立了混合遗传算法。数值计算结果表明，本文所建立的方法对于求解参数识别反问题和非线性优化问题是非常有效的，并且具有良好的鲁棒性和全局收敛能力。     

Application of the theory of branching of solutions of nonlinear equations in the estimation of pole perturbance of linear closed-loop systems

When the state and input matrices of a multivariable linear time-invariant system are perturbed, the problem of the estimation of pole perturbance of closed-loop system is considered by making use of the theory of branching of solutions for nonlinear equations.