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.     

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.     

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.     

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.     

基于子结构的非线性参数系统动力反演方法研究

研究了输入、输出不完备情况下的非线性参数系统动力反演问题.将子结构技术与分解算法相结合,引入广义逆,无需迭代.直接求得待识别参数的极小范数最小二乘解,反演获得未知输入荷载.本文从理论上论证了该方法的收敛性和严格的适用条件,为有限测点条件下非线性参数系统的动力反演问题提供了一个较好的解决方法.与全量补偿算法相比,计算效率大大提高,具有广泛的工程实际应用前景.数值算例表明该方法具有很好的参数识别精度及荷载反演效果.     

非线性最小二乘跟踪的对偶变量变分方法

最小二乘跟踪方法是近几年提出的一种计算动力系统跟踪轨迹的方法.基于最小二乘跟踪的灵敏度分析算法可以有效避免传统的非线性系统灵敏度分析方法中的病态初值问题,因此其在混沌系统灵敏度分析方面有着重要的应用.针对非线性的最小二乘跟踪问题,首先将其重新描述为带有约束的非线性最优控制问题,引入协态变量并将系统的哈密顿函数表示为关于状态变量和协态变量的函数.然后将目标函数的积分时间离散化,根据对偶变量变分原理,以离散区间两端的状态变量作为独立变量,用Lagrange插值多项式近似离散区间内的状态变量和协态变量,进而将非线性最优控制问题转化为求解非线性方程组问题.这种算法无需对原问题做线性化处理,避免了复杂的线性化过程以及可能因此造成的误差,同时为求解非线性最小二乘跟踪问题提供了新的思路.根据最小二乘方法可以得到两条设计参数有微小变化的状态轨迹,基于这两条状态轨迹可进一步计算出系统关于设计参数的灵敏度,范德波振子作为数值算例验证了该方法在求解最小二乘跟踪问题以及计算非线性系统灵敏度时的有效性.      

Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling

This paper focuses on the identification problem of Wiener nonlinear systems with non-uniform sampling. The mathematical model for the Wiener nonlinear system is established from the non-uniformly sampled input–output data. In order to solve the identification problem of the Wiener nonlinear system with the unmeasurable variables in the information vector, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. Finally, the simulation results indicate that the proposed algorithm is effective.     

飞机机翼损伤的气动模型及故障诊断研究简

为了确保机翼损伤后飞机的飞行安全，提出了一种在线的故障诊断方法. 首先，根据输入输出特性，采用遗忘因子递推最小二乘法对飞机故障后的气动导数进行辨识，建立了机翼损伤故障的数学模型；然后，结合多模型方法和中心差分卡尔曼滤波器（central difference Kalman filter，CDKF）各自的优点，实现对机翼损伤的故障诊断，并采用强跟踪滤波器在线更新CDKF 的采样点，以增强CDKF 的自适应能力. 最后，通过仿真结果验证了本文所提方法的有效性.     

Lyapunov stability for continuous-time multidimensional nonlinear systems

This paper deals with the stability of continuous-time multidimensional nonlinear systems in the Roesser form. The concepts from 1D Lyapunov stability theory are first extended to 2D nonlinear systems and then to general continuous-time multidimensional nonlinear systems. To check the stability, a direct Lyapunov method is developed. While the direct Lyapunov method has been recently proposed for discrete-time 2D nonlinear systems, to the best of our knowledge what is proposed in this paper are the first results of this kind on stability of continuous-time multidimensional nonlinear systems. Analogous to 1D systems, a sufficient condition for the stability is the existence of a certain type of the Lyapunov function. A new technique for constructing Lyapunov functions for 2D nonlinear systems and general multidimensional systems is proposed. The proposed method is based on the sum of squares (SOS) decomposition, therefore, it formulates the Lyapunov function search algorithmically. In this way, polynomial nonlinearities can be handled exactly and a large class of other nonlinearities can be treated introducing some auxiliary variables and constrains.     

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.     

Hierarchical recursive least squares algorithm for Hammerstein systems using the filtering method

A hierarchical recursive least squares algorithm is presented in the paper to estimate the parameters of Hammerstein nonlinear systems by combining the filtering method and least squares search principle. The key is to decompose the Hammerstein system into two subsystems by adopting the hierarchical idea. Numerical examples are given to illustrate the performance of the proposed algorithm.     

基于能量辨识的非线性静电-结构系统的自由度缩减建模方法

给出了一种基于系统能量函数辨识的静电致动微薄板系统自由度缩减建模方法.从Von Karman应变-位移关系式出发,推导出以广义模态坐标为变量的系统动能、应变能以及电容函数的函数表达式.为了将应变能以及电容函数写成广义模态坐标的多变量多项式形式,利用一系列经静态非线性结构有限元计算的结果,拟合得到多变量多项式的未知系数.由Lagrangian方程获得原系统的自由度缩减模型.利用该模型对器件的静/动态特性进行仿真,其计算费用很低.与有限元结果比较,验证了建模方法的正确性.     

Newton iterative identification for a class of output nonlinear systems with moving average noises

This paper discusses iterative identification problems for a class of output nonlinear systems (i.e., Wiener nonlinear systems) with moving average noises from input–output measurement data, based on the Newton iterative method. The basic idea is to decompose a nonlinear system into two subsystems, to replace the unknown variables in the information vectors with their corresponding estimates at the previous iteration, and to present a Newton iterative identification method using the hierarchical identification principle. The numerical simulation results indicate that the proposed algorithms are effective.     

A sum of squares approach to robust PI controller synthesis for a class of polynomial multi-input multi-output nonlinear systems

This paper presents a new algorithmic method to design PI controller for a general class of nonlinear polynomial systems. Design procedure can take place on certain or uncertain nonlinear model of plant and is based on sum of squares optimization.The so-called density function is employed to formulate the design problem as a convex optimization program in the sum of squares form. Robustness of design is guaranteed by taking parametric uncertainty into account with an approach similar to that of generalized ${\mathcal {S}}$ -Procedure. Validity and applicability of the proposed methods are verified via numerical simulations. The method presented here for PI controller design is not based on local linearization and works globally. Derived stability conditions overcome several drawbacks seen in previous results, such as depending on a linearized model or a stable model. Furthermore, employing sum of squares technique makes it possible to derive stability conditions with least conservatism and directly design controller for polynomial affine nonlinear systems.     

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.     

Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule

This paper develops a biased compensation recursive least squares-based threshold algorithm for a time-delay rational model. The time-delay rational model is transformed into an augmented model by using the redundant rule, and then, a recursive least squares algorithm is proposed to estimate the parameters of the augmented model. Since the output of the augmented model is correlated with the noise, a biased compensation method is derived to eliminate the bias of the parameter estimates. Furthermore, based on the structures of the augmented model parameter vector and the rational model parameter vector, the unknown time delay can be computed by using a threshold given in prior. A simulated example is used to illustrate the efficiency of the proposed algorithm.     

BLS-based formation control for nonlinear multi-agent systems with actuator fault and input saturation

This paper studies the formation control of a nonlinear multi-agent system based on a broad learning system under actuator fault and input saturation. Firstly, the multi-agent tracking error is proposed based on graph theory. Besides, fault tolerance should be considered when actuator fault exists. Meanwhile, the broad learning system is put forward to approximate the unknown nonlinear function in the multi-agent system. Then, an input saturation auxiliary system is introduced to reduce the adverse effects of input saturation constraints. At the same time, the disturbance observer technology is used to estimate the actuator failure as a lumped uncertainty. At last, dynamic surface control is introduced to realize formation control with actuator fault and input saturation. Obviously, it is difficult to design a controller with unknown nonlinear function, input saturation, and actuator fault existing in the multi-agent system. The Lyapunov method can prove the stability of the formation control. The simulation results verify the effectiveness of the controller.

     

Instrumental variables approach to identification of a class of MIMO Wiener systems

A new approach to identification of multi-input multi-output (MIMO) Wiener systems using the instrumental variables method is presented. It is assumed that static nonlinear elements are invertible and their inverse characteristics can be expressed or approximated by polynomials of known orders. It is also assumed that the linear part of the Wiener system can be represented by a matrix polynomial form. Based on these assumptions, the Wiener system is transformed introducing a new parameterization and its parameters are estimated using a linear-in-parameters model. To solve the problem of non-consistency of least squares parameter estimates, an instrumental variables method is employed. A numerical example is included to show the effectiveness and the practical feasibility of the presented approach.     

基于北斗信号辐射源的无源雷达定位技术

外辐射源无源跟踪对提高区域防御系统的生存能力和反击能力具有重要的军事意义和实用价值。近年来,基于星载辐射源的无源雷达探测系统受到广泛关注。在选取北斗卫星信号为研究对象的基础上,首先从模糊函数和雷达探测距离方面重点分析了北斗信号作为无源雷达辐射源的实用性。然后研究了一种基于时差测量的多发单收型无源雷达定位系统,并给出了解算目标位置的总体最小二乘算法。最后对该系统下的定位算法和定位精度进行分析,仿真结果表明在相同条件下,总体最小二乘算法能减小近50%的定位误差,且误差波动范围明显减小。     

Nonlinear model predictive control based on piecewise linear Hammerstein models

This paper develops a nonlinear model predictive control (MPC) algorithm for dynamic systems represented by piecewise linear (PWL) Hammerstein models. At each sampling instant, the predicted output trajectory is linearized online at an assumed input trajectory such that the control actions can be easily calculated by solving a quadratic programming optimization problem, and such linearization and optimization may be repeated a few times for good linear approximation accuracy. A three-step procedure is developed to linearize a PWL function, where the derivatives of a PWL function are obtained by a computationally efficient look-up table approach. Unlike many existing MPC algorithms for Hammerstein systems, it does not require the inversion of static nonlinearity and can directly cope with input constraints even in multivariable systems. Two benchmark chemical reactors are studied to illustrate the effectiveness of the proposed algorithm.