非线性系统执行器死区故障的鲁棒自适应控制

针对一类具有不对称执行器死区故障的不确定非线性系统,基于反推滑模控制原理提出了一种神经网络鲁棒自适应控制方案。通过简化死区故障模型,取消了模型倾斜度相等和边界对称条件,结合动态面控制避免了传统反推设计方法存在的计算复杂性问题。所提控制方案取消了控制方向已知的条件,消除了执行器死区故障的影响,使得系统输出趋于给定参考轨迹的一个小领域。仿真结果验证了该方法的有效性。     

Adaptive variable structure control for large-scale time-delayed systems with unknown nonlinear dead-zone

The problem of adaptive fuzzy control for a class of large-scale, time-delayed systems with unknown nonlinear dead-zone is discussed here. Based on the principle of variable structure control, a design scheme of adaptive, decentralized, variable structure control is proposed. The approach removes the conditions that the dead-zone slopes and boundaries are equal and symmetric, respectively. In addition, it does not require that the assumptions that all parameters of the nonlinear dead-zone model and the lumped uncertainty are known constants. The adaptive compensation terms of the approximation errors are adopted to minimize the inuence of modeling errors and parameter estimation errors. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded, with tracking errors converging to zero. Simulation results demonstrate the effectiveness of the approach.     

Stochastic differential equations and stochastic linear quadratic optimal control problem with Lévy processes

In this paper, the authors first study two kinds of stochastic differential equations (SDEs) with Lévy processes as noise source. Based on the existence and uniqueness of the solutions of these SDEs and multi-dimensional backward stochastic differential equations (BSDEs) driven by Lévy processes, the authors proceed to study a stochastic linear quadratic (LQ) optimal control problem with a Lévy process, where the cost weighting matrices of the state and control are allowed to be indefinite. One kind of new stochastic Riccati equation that involves equality and inequality constraints is derived from the idea of square completion and its solvability is proved to be sufficient for the well-posedness and the existence of optimal control which can be of either state feedback or open-loop form of the LQ problems. Moreover, the authors obtain the existence and uniqueness of the solution to the Riccati equation for some special cases. Finally, two examples are presented to illustrate these theoretical results. This work was supported by the National Basic Research Program of China (973 Program) under Grant No. 2007CB814904, the Natural Science Foundation of China under Grant No. 10671112 and Shandong Province under Grant No. Z2006A01, and Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20060422018.     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

TWO-STAGE ESTIMATION FOR SEEMINGLY UNRELATED NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated （SU） non- parametric regression models. The authors propose a new method to estimate the unknown functions, which is an extension of the two-stage procedure in the longitudinal data framework. The authors show the resulted estimators are asymptotically normal and more efficient than those based on only the individual regression equation. Some simulation studies are given in support of the asymptotic results. A real data from an ongoing environmental epidemiologie study are used to illustrate the proposed procedure.     

基于2-D系统理论的变初始条件离散系统的迭代学习控制

对于具有重复运动特性的系统，迭代学习控制是一种简单有效的控制方法。为便于实时应用，所有迭代学习控制方案的设计必须在离散时间域进行。初始状态问题是学习控制设计中遇到的一个重要问题。针对具有变初始状态的离散时间系统，利用2-D线性连续．离散型系统理论设计了闭环迭代学习控制器，并给出了保证控制器收敛的充分必要条件。仿真结果证明了该方案的有效性。     

Optimal Investment with Noise Trading Risk

This paper investigates the optimal dynamic investment for an investor who maximizes constant absolute risk aversion （CARA） utility in a discrete-time market with a riskfree bond and a risky stock. The risky stock is assumed to present both the dividend risk and the price risk. With our assumptions, the dividend risk is equivalent to fundamental risk, and the price risk is equivalent to the noise trading risk. The analytical expression for the optimal investment strategy is obtained by dynamic programming. The main result in this paper highlights the importance of differentiating between noise trading risk and fundamental risk for the optimal dynamic investment.     

基于Shifted Legendre正交多项式的迭代学习控制方法

考虑用迭代学习控制方法来解决一类线性时变连续系统的终端控制问题。运用ShiftedLegendre正交多项式的展开技术,利用其正交性和边值条件,将线性时变系统的微分方程转化为代数方程,避免了在判断误差收敛条件的过程中求解线性时变系统状态转移矩阵。并采用高阶学习律来求控制输入的ShiftedLegendre系数向量,仿真实例验证了该方法的有效性。     

双柔臂空间机器人运动、振动一体化抗死区控制

探讨漂浮基双柔臂空间机器人存在关节力矩输出死区与外部干扰时的姿态角度运动跟踪及柔性抑振的控制问题。引入含有刚性运动与柔性模态的混合轨迹概念,提出基于运动、振动一体化的积分滑模神经网络自适应控制方案。此方案的优点是不但通过补偿项消除了死区斜率与边界参数不确定及最优逼近误差上确界未知的影响,确保了刚性运动的鲁棒性,而且能同时主动地抑制两臂杆的柔性振动,从而提高轨迹跟踪性能。仿真算例结果表明方案能有效地实现运动、振动一体化控制。     

A Priority-Based Negotiations Approach for Handling Inconsistencies in Multi-Perspective Software Requirements

Inconsistency of multi-perspective requirements specifications is a pervasive issue during the requirements process. However, managing inconsistency is not just a pure technical problem. It is always associated with a process of interactions and competitions among corresponding stakeholders. The main contribution of this paper is to present a negotiations approach to handling inconsistencies in multi-perspective software requirements. In particular, the priority of requirements relative to each perspective plays an important role in proceeding negotiation over resolving inconsistencies among different stakeholders. An algorithm of generating negotiation proposals and an approach to evaluating proposals are also presented in this paper, respectively.     

分数阶非线性时滞系统的P型迭代学习控制

针对一类分数阶非线性时滞系统,研究其P型迭代学习控制问题。首先,根据分数微积分的基本性质并借助推广的Gronwall不等式,获得系统状态变量的范数不等式估计;然后,通过引入λ-范数,获得在开环和闭环P型迭代学习控制作用下,系统控制输入以及跟踪误差收敛的充分条件。所得结果与已有结果相比,具有更小的保守性。最后,通过数值仿真验证所提方法的有效性。     

基于2D模型的网络系统迭代学习控制设计方法

迭代学习控制(iterative learning control, ILC)方法应用于网络控制系统时,由于数据需要在控制器和远程对象间传输经常产生数据丢失现象。给出了一种存在数据丢失时网络系统的随机迭代学习控制设计方法,首先将数据丢失现象描述为随机伯努利序列,在此基础上将迭代学习的控制器设计转化为随机〖JP2〗2D Roesser系统的稳定问题。定义了随机意义下2D系统的均方渐进稳定,基于线性矩阵不等式(linear matrix inequality, LMI)给出一个判别稳定性的条件,该条件同时可实现迭代学习控制器的设计。仿真示例验证了设计方法的有效性。     

Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.     

基于T-S模型的迭代学习控制算法及其在机器人点位控制中的仿真研究

考虑了一类特殊的迭代学习控制问题，即用迭代学习方法解决机器人的点位控制问题。采用T-S模型描述机器人系统，在T-S模型的基础上，运用并行分配补偿方法(PDC)确定T-S模型的迭代学习控制器结构，并给出了误差收敛条件。为避免迭代过程的初始定位操作，丈中还设计了模糊循环迭代学习律。最后以在垂直面内运动的单关节的机器人为例说明了所提出方法的有效性。     

基于迭代学习控制的PID控制器设计

针对传统的经验PID整定方法,提出了一种新的PID参数整定算法。该算法首先利用PD型迭代学习控制来进行期望轨迹的跟踪控制,然后根据迭代学习控制的输入输出数据序列,通过强跟踪滤波器来进行参数辨识,可获得对应于期望轨迹的优化的PID控制参数。给出了迭代学习控制的收敛条件,以及如何利用强跟踪滤波器来进行参数辨识。仿真和实验结果表明,采用该算法设计PID控制器,被控系统可以获得较佳的动态性能和较强的鲁棒性。     

The projective plane crossing numbers of circular graphs*

The authors give an upper bound for the projective plane crossing number of a circular graph. Also, the authors prove the projective plane crossing numbers of circular graph C （8, 3） and C （9, 3） are 2 and 1, respectively.     

ON THE ALMOST SURELY ASYMPTOTIC BOUNDS OF A CLASS OF ORNSTEIN-UHLENBECK PROCESSES IN INFINITE DIMENSIONS*

Given a real-valued separable M-type 2 Banach space X with the p-th power of the norm of C2-class, the almost sure asymptotic upper bounds of the solutions of the Ornstein-Uhlenbeck Processes described by the following equations {dz（t）=A（t,z（t））dt＋σ（t,z（t））dW（t）,z（0）=z0∈X,are investigated. This study generalizes the corresponding well-known finite dimensional results of Lapeyre （1989） and Mao （1992）.     

Adaptive Robust Dead-Zone Compensation Control of Electro-Hydraulic Servo Systems with Load Disturbance Rejection

A backstepping method based adaptive robust dead-zone compensation controller is proposed for the electro-hydraulic servo systems(EHSSs) with unknown dead-zone and uncertain system parameters.Variable load is seen as a sum of a constant and a variable part.The constant part is regarded as a parameter of the system to be estimated real time.The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them.Compared with the traditional dead-zone compensation method,a dead-zone compensator is incorporated in the EHSS without constructing a dead-zone inverse.Combining backstepping method,an adaptive robust controller(ARC) with dead-zone compensation is formed.An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure.Simulations show that the proposed method has a splendid tracking performance,all the uncertain parameters can be estimated,and the disturbance has been rejected while the dead-zone term is well estimated and compensated.     

Remarks on Network Community Properties

This paper discusses a popular community definition in complex network research in terms of the conditions under which a community is minimal, that is, the community cannot be split into several smaller communities or split and reorganized with other network elements into new communities. The result provides a base on which further optimization computation of the quantitative measure for community identification can be realized.     

Convergence analysis in sense of Lebesgue-p norm of decentralized non-repetitive iterative learning control for linear large-scale systems

In this paper, a decentralized iterative learning control strategy is embedded into the procedure of hierarchical steady-state optimization for a class of linear large-scale industrial processes which consists of a number of subsystems. The task of the learning controller for each subsystem is to iteratively generate a sequence of upgraded control inputs to take responsibilities of a sequential step functional control signals with distinct scales which are determined by the local decision-making units in the two-layer hierarchical steady-state optimization processing. The objective of the designated strategy is to consecutively improve the transient performance of the system. By means of the generalized Young inequality of convolution integral, the convergence of the learning algorithm is analyzed in the sense of Lebesgue-p norm. It is shown that the inherent feature of system such as the multi-dimensionality and the interaction may influence the convergence of the non-repetitive learning rule. Numerical simulations illustrate the effectiveness of the proposed control scheme and the validity of the conclusion. This research was supported by the National Natural Science Foundation of China under Grant No. F030101–60574021.