Global Stabilization of Nonlinear Systems with Byrnes—Isidori Normal Form

1　IntroductionThe problem of the asymptotic stabilization of minimum phase nonlinear systems viasmooth state feedback has been well studied in[1— 6 ].One major methodology is by useof the nonlinear analog of the nonlinear zero dynamics notion so that,un…     

ASYMPTOTIC ANALYSIS OF THE OPTIMAL CONTROL OF AN ELASTODYNAMIC SYSTEM

Based on the idea of the reduction of optimal control systems in singularperturbations of Lions and a priori estimates in [4], we prove the convergence of theoptimal control, state and the cost function between a singular linear elastodynamic systemand a limit system. Our asymptotic analysis is applicable to the optimal control of flexiblerobotic manipulators.     

Inference on coefficient function for varying-coefficient partially linear model

One important model in handling the multivariate data is the varying-coemcient partially linear regression model.In this paper,the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not.It is showed that the normalized proposed test follows asymptoticallyχ~2-distribution and the Wilks phenomenon under the null hypothesis,and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing.Some simulation studies illustrate that the test works well.     

RESIDUAL A POSTERIORI ERROR ESTIMATE OF A NEW TWO-LEVEL METHOD FOR STEADY NAVIER-STOKES EQUATIONS

Residual-based a posteriori error estimate for conforming finite element solutions of incom-pressible Navier-Stokes equations,which is computed with a new two-level method that is differentfrom Volker John,is derived.A posteriori error estimate contains additional terms in comparison tothe estimate for the solution obtained by the standard finite element method.The importance of theadditional terms in the error estimates is investigated by studying their asymptotic behavior.For opti-mal scaled meshes,these bounds are not of higher order than the convergence of discrete solution.Thetwo-level method aims to solve the nonlinear problem on a coarse grid with less computational work,then to solve the linear problem on a fine grid,which is superior to the usual finite element methodsolving a similar nonlinear problem on the fine grid.     

Positivity and Stability of Fractional-Order Linear Time-Delay Systems

This article focuses on the positivity and the asymptotic stability of fractional-order linear time-delay systems (FOLTDSs) which are composed of N (N ≥ 2) subsystems. Firstly, a sufficient and necessary condition is given to ensure the positivity of FOLTDSs. The solutions of the studied systems are obtained by using the Laplace transform method, and it can be observed that the positivity of FOLTDSs is completely determined by the series of matrices and independent of the magnitude of time-delays. Secondly, a theorem is given to prove the asymptotic stability of positive FOLTDSs. By considering the monotonicity and asymptotic properties of systems with constant time-delay, it is further shown that the asymptotic stability of positive FOLTDSs is independent of the time-delay. Next, a state-feedback controller, whose gain matrix is derived by resolving a linear programming question, is designed such that the state variables of the systems are nonnegative and asymptotically convergent. When the order of the FOLTDSs is greater than one, by utilizing a proposed property of Caputo derivative, a sufficient condition for the positivity of FOLTDS is presented. Finally, simulation examples are presented to verify the validity and practicability of the theoretical analysis.

     

Asymptotic and stable proper ties of general stochastic functional differential equations

The asymptotic and stable properties of general stochastic functional differential equations are investigated by the multiple Lyapunov function method, which admits non-negative up-per bounds for the stochastic derivatives of the Lyapunov functions, a theorem for asymptotic properties of the LaSal e-type described by limit sets of the solutions of the equations is obtained. Based on the asymptotic properties to the limit set, a theorem of asymptotic stability of the stochastic functional differential equations is also established, which enables us to construct the Lyapunov functions more easily in application. Particularly, the wel-known classical theorem on stochastic stability is a special case of our result, the operator LV is not required to be negative which is more general to fulfil and the stochastic perturbation plays an important role in it. These show clearly the improvement of the traditional method to find the Lyapunov functions. A numerical simulation example is given to il ustrate the usage of the method.     

STOCHASTIC SYSTEM IDENTIFICATION VIA ADAPTIVE SPECTRAL FACTORIZATION

We consider in this paper the problem of recursive identification for stochastic systemswhen the noise model does not satisfy the positive real condition associated with convergence ofstandard algorithms.To avoid the positive real condition,adaptive spectral factorization techniquesare exploited on the basis of a class of non-standard time-varying recursive Riccati equations.Theasymptotic properties of the Riccati equations are studied as a crucial step to the convergence resultsof the paper.     

离散线性区间系统的分支稳定性

研究了离散区间系统的一种特殊形式的稳定性 ,即分支稳定性。分别给出了离散区间系统分支渐近稳定性和指数分支渐近稳定性的定义。证明了离散区间系统是分支渐近稳定的 ,且仅当系统矩阵在每一时刻都将满足一个约束条件的系统状态向量变换成满足另一个约束的向量。由此利用不等式的性质分别给出了离散区间系统分支渐近稳定和指数分支渐近稳定的充要条件     

LIMIT THEOREMS AND OPTIMAL DESIGN WITH ADAPTIVE URN MODELS

In this paper we study urn model, using some available estimates of successes probabilities, and adding particle parameter, we establish adaptive models. We obtain some strong convergence theorems, rates of convergence, asymptotic normality of components in the urn, and estimates. With these asymptotical results, we show that the adaptive designs given in this paper are asymptotically optimal designs.     

FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs

This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to characterize the input's persistent excitation, under which the strong convergence and the convergence rate of the two-step estimation algorithm are given. As for the asymptotical efficiency,with a suitable selection of the weighting matrix in the algorithm, even though the limit of the product of the Cram′er-Rao(CR) lower bound and the data length does not exist as the data length goes to infinity, the estimates still can be asymptotically efficient in the sense of CR lower bound. A numerical example is given to demonstrate the effectiveness and the asymptotic efficiency of the algorithm.     

基于强跟踪滤波器的改进非线性自适应观测器

对基于强跟踪滤波器的非线性自适应观测器(nonlinearadaptiveobserver,NAO)的收敛性进行了分析,给出了NAO局部渐近收敛的充分条件。提出了一种改进非线性自适应观测器(modifiednonlinearadaptiveobserver,MNAO)算法。MNAO在具有强跟踪特性的同时对输出测量中的坏数据有较强的鲁棒性。为了降低对初始误差的敏感性,采用一种强跟踪扩展卡尔曼观测器算法启动MNAO。数值仿真示例显示了本方法的有效性。     

Asymptotic Optimality of the Nonnegative Garrote Estimator Under Heteroscedastic Errors

This paper proposes the Nonnegative Garrote(NG) estimator for linear model with heteroscedastic errors. On the other hand, under some regularity conditions, the authors show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model average estimator. Simulation results and a real data analysis are reported for testing the results obtained previously. These results provide a stronger theoretical basis for the use of NG estimator by strengthening existing findings.     

Signal estimation with binary-valued sensors

<正> This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.     

Generalized F-test for high dimensional regression coefficients of partially linear models

This paper proposes a test procedure for testing the regression coefficients in high dimensional partially linear models based on the F-statistic. In the partially linear model, the authors first estimate the unknown nonlinear component by some nonparametric methods and then generalize the F-statistic to test the regression coefficients under some regular conditions. During this procedure, the estimation of the nonlinear component brings much challenge to explore the properties of generalized F-test. The authors obtain some asymptotic properties of the generalized F-test in more general cases,including the asymptotic normality and the power of this test with p/n ∈(0, 1) without normality assumption. The asymptotic result is general and by adding some constraint conditions we can obtain the similar conclusions in high dimensional linear models. Through simulation studies, the authors demonstrate good finite-sample performance of the proposed test in comparison with the theoretical results. The practical utility of our method is illustrated by a real data example.     

比例延迟微分方程组Rosenbrock方法的渐近稳定性

讨论用一类变步长Rosenbrock方法求解线性比例延迟微分方程组的渐近稳定性,证明了在无穷远点严格稳定的变步长Rosenbrock方法能够保持原线性系统的渐近稳定性。数值试验进一步验证了算法的理论分析的正确性。     

目标轨线迭代可变的非线性系统自适应学习控制

针对含有混合参数的非线性不确定系统,提出了一种自适应迭代学习控制方案。该方案利用Back-stepping方法和参数重组技巧相结合,可以处理目标轨线迭代可变的跟踪问题。通过引入微分-差分自适应学习律,设计了一种自适应控制策略,使得跟踪误差在一个有限区间上的积分渐近收敛于零;通过构造Lyapunov-like函数,给出了闭环系统收敛的一个充分条件。数值仿真验证了所提方法的可行性。     

Stability analysis of extended discrete-time BAMneural networks based on LMI approach

1 .INTRODUCTIONBidirectional associative memory model is a kind ofneural network models in common use with the abili-ty of information memory and association. Since thedistributed memory of the information,the networkcan associate a completed and clear mode stored in itfroman uncompleted and fuzzy mode . Bidirectionalassociative memory (BAM) proposed by B. KoskoinRef .[1] is a generalization of Cohen-Grossberg’smodel from single layer to two layers . Since then,there have beenlots of …     

Inertial projection algorithms for convex feasibility problem

The purpose of this paper is to apply inertial technique to string averaging projection method and block-iterative projection method in order to get two accelerated projection algorithms for solving convex feasibility problem.Compared with the existing accelerated methods for solving the problem,the inertial technique employs a parameter sequence and two previous iterations to get the next iteration and hence improves the flexibility of the algorithm.Theoretical asymptotic convergence results are presented under some suitable conditions.Numerical simulations illustrate that the new methods have better convergence than the general projection methods.The presented algorithms are inspired by the inertial proximal point algorithm for finding zeros of a maximal monotone operator.     

PDα-型分数阶迭代学习控制在Lp范数意义下的收敛性分析

针对一类分数阶线性系统,讨论了PD^α-型分数阶迭代学习控制算法的单调收敛性。首先,在Lebesgue^p(L^p)范数意义下,对一、二阶PD^α-型控制算法的单调收敛性进行理论分析,推导出其单调收敛的充分条件,并推广到N阶控制算法的情形;然后,对二者的收敛快慢进行了详细说明。结论表明,控制算法的收敛条件由学习增益和系统自身属性共同决定。仿真实验验证了理论的正确性和控制算法的可行性。     

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

This paper applies bootstrap methods to LM tests(including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu(2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((N T)~(-2)) and that of fast double bootstrap LM tests is O((NT)~(-5/2)). Extensive Monte Carlo experiments suggest that,compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level.Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.