Two‐stage auxiliary model gradient‐based iterative algorithm for the input nonlinear controlled autoregressive system with variable‐gain nonlinearity

This article focuses on the parameter estimation problem of the input nonlinear system where an input variable‐gain nonlinear block is followed by a linear controlled autoregressive subsystem. The variable‐gain nonlinearity is described analytical by using an appropriate switching function. According to the gradient search technique and the auxiliary model identification idea, an auxiliary model‐based stochastic gradient algorithm with a forgetting factor is presented. For the sake of improving the parameter estimation accuracy, an auxiliary model gradient‐based iterative algorithm is proposed by utilizing the iterative identification theory. To further optimize the performance of the algorithm, we decompose the identification model of the system into two submodels and derive a two‐stage auxiliary model gradient‐based iterative (2S‐AM‐GI) algorithm by using the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms and show that the 2S‐AM‐GI algorithm has higher identification efficiency compared with the other two algorithms.     

Performance Analysis of The Auxiliary‐Model‐Based Multi‐Innovation Stochastic Newton Recursive Algorithm for Dual‐Rate Systems

The stochastic Newton recursive algorithm is studied for dual‐rate system identification. Owing to a lack of intersample measurements, the single‐rate model cannot be identified directly. The auxiliary model technique is adopted to provide the intersample estimations to guarantee the recursion process continues. Intersample estimations have a great influence on the convergence of parameter estimations, and one‐step innovation may lead to a large fluctuation or even divergence during the recursion. In the meantime, the sample covariance matrix may appear singular. The recursive process would cease for these reasons. In order to guarantee the recursion process and to also improve estimation accuracy, multi‐innovation is utilized for correcting the parameter estimations. Combining the auxiliary model and multi‐innovation theory, the auxiliary‐model‐based multi‐innovation stochastic Newton recursive algorithm is proposed for time‐invariant dual‐rate systems. The consistency of this algorithm is analyzed in detail. The final simulations confirm the effectiveness of the proposed algorithm.     

Three‐stage multi‐innovation parameter estimation for an exponential autoregressive time‐series model with moving average noise by using the data filtering technique

This paper studies the data filtering‐based identification algorithms for an exponential autoregressive time‐series model with moving average noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into three sub‐identification (Sub‐ID) models, and a filtering‐based three‐stage extended stochastic gradient algorithm is derived for identifying these Sub‐ID models. In order to improve the parameter estimation accuracy, a filtering‐based three‐stage multi‐innovation extended stochastic gradient (F‐3S‐MIESG) algorithm is developed by using the multi‐innovation identification theory. The simulation results indicate that the proposed F‐3S‐MIESG algorithm can work well.     

Training algorithm for radial basis function neural network based on quantum-behaved particle swarm optimization

Radial basis function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. The existing training algorithms, such as orthogonal least squares (OLS) algorithm, clustering and gradient descent algorithm, have their own shortcomings respectively. In this paper, we propose a training algorithm based on a novel population-based evolutionary technique, quantum-behaved particle swarm optimization (QPSO), to train RBF neural network. The proposed QPSO-trained RBF network was tested on non-linear system identification problem and chaotic time series forecasting problem, and the results show that it can identify the system and forecast the chaotic time series more quickly and precisely than that trained by the particle swarm algorithm.     

具有分片非线性输入的Wiener系统的随机梯度迭代(英文)

研究了一类具有分片非线性输入(又称为死区非线性输入)的Wiener系统的参数辨识,分片非线性输入是一个强非线性输入,其数学模型不能写成参数乘以输入的形式,首先引入开关函数,接着利用开关函数将原系统的不可辨识模型转换为可辨识模型,然后通过随机梯度迭代方法辨识出系统的参数,利用辨识出的参数可以计算出系统所有待辨识参数。仿真结果证明了本文方法的有效性。     

Cloud‐based parallel solution for estimating statistical significance of megabyte‐scale DNA sequences

Confidence in a pairwise local sequence alignment is a fundamental problem in bioinformatics. For huge DNA sequences, this problem is highly compute‐intensive because it involves evaluating hundreds of local alignments to construct an empirical score distribution. Recent parallel solutions support only kilobyte‐scale sequence sizes and/or are based on sophisticated infrastructures that are not available for most of the research labs. This paper presents an efficient parallel solution for evaluating the statistical significance for a pair of huge DNA sequences using cloud infrastructures. This solution can receive requests from various researchers via web‐portal and allocate resources according to their demand. In this way, the benefits of cloud‐based services can be achieved. The fundamental innovation of this research work is proposing an efficient solution that utilizes both shared and distributed memory architectures via cloud technology to enhance the performance of evaluating the statistical significance for pair of DNA sequences. Therefore, the restriction on the sequence sizes is released to be in megabyte‐scale, which was not supported before for the statistical significance problem. The performance evaluation of the proposed solution was carried out on Microsoft's cloud and compared with the existing parallel solutions. The results show that the processing speed outperforms the recent cluster solutions that target the same problem. In addition, the performance metrics exhibit linear behavior for the addressed number of instances. Copyright © 2012 John Wiley & Sons, Ltd.     

Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory

This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods.     

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

This paper presents a methodology for on‐line closed‐loop identification of a class of nonlinear servomechanisms. First, a system is defined with the same structure as the actual servomechanism, but using time‐varying estimated parameters. No coupling between the actual and the estimation systems is present. Position, velocity and acceleration errors, defined as the difference of the actual respective signals and the signals coming from the estimation system, are required in the identification method. Then, a recursive algorithm for on‐line identification of the system parameters is derived from a cost function depending on a linear combination of all the estimation errors. Velocity and acceleration estimates, required in the proposed parameter identification algorithm, are obtained by using an algebraic methodology. The identification algorithm is compared by means of real‐time experiments with an on‐line least squares algorithm with forgetting factor and an off‐line least squares algorithm with data preprocessing. Experimental results show that the proposed approach has a performance comparable to that obtained with the off‐line least squares algorithm, but with the advantage of avoiding any preprocessing.     

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real‐time observed data, a cost function with dynamical data is constructed to capture on‐line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.     

Improved delay‐range‐dependent stability analysis for uncertain retarded systems based on affine Wirtinger‐inequality

This note considers the problem of deriving sufficient delay‐range‐dependent stability (DRDS) condition for a general class of uncertain (or nonlinear) retarded system. The stability result is developed by proposing appropriate Lyapunov–Krasovskii functional to effectively utilize the merit of recently proposed affine Wirtinger inequality. The delay upper bound results obtained by the developed condition are found to be less conservative compared with recent results for both nominal and uncertain systems. Copyright © 2017 John Wiley & Sons, Ltd.     

A UD factorization‐based nonlinear adaptive set‐membership filter for ellipsoidal estimation

The extended set‐membership filter (ESMF) for nonlinear ellipsoidal estimation suffers from numerical instability, computation complexity as well as the difficulty in filter parameter selection. In this paper, a UD factorization‐based adaptive set‐membership filter is developed and applied to nonlinear joint estimation of both time‐varying states and parameters. As a result of using the proposed UD factorization, combined with a new sequential and selective measurement update strategy, the numerical stability and real‐time applicability of conventional ESMF are substantially improved. Furthermore, an adaptive selection scheme of the filter parameters is derived to reduce the computation complexity and achieve sub‐optimal estimation. Simulation results have shown the efficiency and robustness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd.     

An improved empirical large‐signal model for the GaAs‐ and GaN‐based HEMTs

A complete empirical large‐signal model for the GaAs‐ and GaN‐based HEMTs is presented. Three generalized drain current I–V models characterized by the multi‐bias Pulsed I–V measurements are presented along with their dependence on temperature and quiescent bias state. The new I–V equations dedicated for different modeling cases are kept accurate enough to the higher‐order derivatives of drain‐current. Besides, an improved charge‐conservative gate charge Q–V formulation is proposed to extract and model the nonlinear gate capacitances. The composite nonlinear model is shown to accurately predict the S‐parameters, large‐signal power performances as well as the two‐tone intermodulation distortion products for various types of GaAs and GaN HEMTs. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

High‐gain nonlinear observer‐based impedance control for deformable object cooperative teleoperation with nonlinear contact model

Unmeasurable object deformation and local communication time delays between the slave robots influence the manipulation effect for multirobot multioperator teleoperation. In this article, a distributed control method based on high‐gain nonlinear observer, interactive identification, and impedance control is proposed for this problem. First, we use Hunt‐Crossley contact model and deduce the desired synchronizing object state in cooperative teleoperation. Second, an impedance item expressed by the internal position errors is presented to decrease object position tracking errors. For the unmeasurable object deformation, an interactive identification method is proposed for estimating unknown variables. Third, we consider both varying communication time delays and local time delays in the slave side. Two mirror high‐gain nonlinear observers are designed for estimating other slave robots' real‐time state. Finally, we build the system controllers and prove the stability of the closed‐loop system and the boundless of estimating errors using Lyapunov functions. Comparable simulation results executed by the physical system present that the position and internal force tracking errors of the object decrease in the designated cooperative tasks.     

Tube‐based robust nonlinear model predictive control

This paper extends tube‐based model predictive control of linear systems to achieve robust control of nonlinear systems subject to additive disturbances. A central or reference trajectory is determined by solving a nominal optimal control problem. The local linear controller, employed in tube‐based robust control of linear systems, is replaced by an ancillary model predictive controller that forces the trajectories of the disturbed system to lie in a tube whose center is the reference trajectory thereby enabling robust control of uncertain nonlinear systems to be achieved. Copyright © 2011 John Wiley & Sons, Ltd.     

Recursive filtering adaptive fault‐tolerant tracking control for uncertain switched multivariable nonlinear systems

This article synthesizes a recursive filtering adaptive fault‐tolerant tracking control method for uncertain switched multivariable nonlinear systems. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. A piecewise constant adaptive law generates adaptive parameters by solving the error dynamics with the neglection of unknowns, and the recursive least squares is employed to minimize the residual error by categorizing the total uncertainty estimates into matched and mismatched components. A filtering control law is designed to compensate the actuator faults and nonlinear uncertainties such that a good tracking performance is delivered with guaranteed robustness. The matched component is canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is performed to eliminate the effect of the mismatched component on the output. By exploiting the average dwell time principle, the error bounds are derived for the states and control inputs compared with the virtual reference system which defines the best performance that can be achieved by the closed‐loop system. Both numerical and practical examples are provided to illustrate the effectiveness of the proposed switching recursive filtering adaptive fault‐tolerant tracking control architecture, comparisons with model reference adaptive control are also carried out.     

A moving switching sequence approach for nonlinear model predictive control of switched systems with state‐dependent switches and state jumps

In this paper, we propose an approach for real‐time implementation of nonlinear model predictive control (NMPC) for switched systems with state‐dependent switches called the moving switching sequence approach. In this approach, the switching sequence on the horizon moves to the present time at each time as well as the optimal state trajectory and the optimal control input on the horizon. We assume that the switching sequence is basically invariant until the first predicted switching time reaches the current time or a new switch enters the horizon. This assumption is reasonable in NMPC for systems with state‐dependent switches and reduces computational cost significantly compared with the direct optimization of the switching sequence all over the horizon. We update the switching sequence by checking whether an additional switch occurs or not at the last interval of the present switching sequence and whether the actual switch occurs or not between the current time and the next sampling time. We propose an algorithm consisting of two parts: (1) the local optimization of the control input and switching instants by solving the two‐point boundary‐value problem for the whole horizon under a given switching sequence and (2) the detection of an additional switch and the reconstruction of the solution taking into account the additional switch. We demonstrate the effectiveness of the proposed method through numerical simulations of a compass‐like biped walking robot, which contains state‐dependent switches and state jumps.     

Parameter estimation for block‐oriented nonlinear systems using the key term separation

This article considers the parameter estimation problems of block‐oriented nonlinear systems. By using the key term separation, the system output is represented as a linear combination of unknown parameters. We give a key term separation auxiliary model gradient‐based iterative (KT‐AM‐GI) identification algorithm and propose a key term separation auxiliary model three‐stage gradient‐based iterative (KT‐AM‐3S‐GI) identification algorithm by using the hierarchical identification principle. Meanwhile, the multiinnovation theory is used to derived the key term separation auxiliary model three‐stage multiinnovation gradient‐based iterative (KT‐AM‐3S‐MIGI) algorithm. The analysis shows that compared with the KT‐AM‐GI algorithm, the KT‐AM‐3S‐GI algorithm can improve the parameter estimation accuracy and reduce the computational burden. In addition, the KT‐AM‐3S‐MIGI can give more accurate parameter estimates than the KT‐AM‐3S‐GI algorithm and can track time‐varying parameters based on the dynamical window data. This work provides a reference for improving the identification performance of multiinput nonlinear output‐error systems or multivariable nonlinear systems. The simulation results confirm the effectiveness of the proposed algorithm.     

On reachable set estimation of two‐dimensional systems described by the Roesser model with time‐varying delays

In this paper, the problem of reachable set estimation of two‐dimensional (2‐D) discrete‐time systems described by the Roesser model with interval time‐varying delays is considered for the first time. New 2‐D weighted summation inequalities, which provide a tighter lower bound than the commonly used Jensen summation inequality, are proposed. Based on the Lyapunov‐Krasovskii functional approach, and by using the 2‐D weighted summation inequalities presented in this paper, new delay‐dependent conditions are derived to ensure the existence of an ellipsoid that bounds the system states in the presence of bounded disturbances. The derived conditions are expressed in terms of linear matrix inequalities, which can be solved by various computational tools to determine a smallest possible ellipsoidal bound. Applications to exponential stability analysis of 2‐D systems with delays are also presented. The effectiveness of the obtained results are illustrated by numerical examples.     

A parameter‐reduced volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions

A nonlinear dynamic behavioral model for radio frequency power amplifiers is presented. It uses orthonormal basis functions, Kautz functions, with complex poles that are different for each nonlinear order. It has the same general properties as Volterra models, but the number of parameters is significantly smaller. Using frequency weighting the out‐of‐band model error can be reduced. Using experimental data it was found that the optimal poles were the same for different input powers and for the different nonlinear orders. The optimal poles were also the same for direct and inverse models, which could be explained theoretically to be a general property of nonlinear systems with negligible linear memory effects. The model can be used as either a direct or inverse model with the same model error for power amplifiers with negligible linear memory effects. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Observer‐based fault estimators using iterative learning scheme for nonlinear time‐delay systems with intermittent faults

This paper deals with the intermittent fault estimation problem for a class of nonlinear time‐delay systems with measurement noise. The time delays are assumed to occur in state vector, nonlinear term as well as output vector, thus reflecting the time delays influence in reality more closely. The aim of the problem is to estimate the intermittent fault by using iterative learning scheme, with the property of index, hence attenuating the influence from measurement noise. Different from existing fault estimating schemes, the state error information and fault estimating information in the previous iteration are used in the current iteration to improve the estimating results. The stability and convergence of iterative learning observer and uniform boundedness of dynamic error system are achieved by using Lyapunov function and optimal function design. Simultaneously, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality by the Schur complements and Young relations. Furthermore, the results are both suited for the systems with time‐varying delay and the systems with constant delay. Finally, two numerical examples are proposed to illustrate the effectiveness of the proposed method, and a comparability example is presented to demonstrate its superiority. Copyright © 2017 John Wiley & Sons, Ltd.