Robust H ∞ Output Tracking Control for a Class of Nonlinear Systems with Time-Varying Delays

This paper addresses the H _∞ output tracking problem for a class of nonlinear systems subjected to model uncertainties and with interval time-varying delay. The stability of the nonlinear time-delay system is analyzed with a novel delay-interval-dependent Lyapunov–Krasovskii functional. Compared to state-of-the-art criteria for linear and nonlinear time-delay systems, less conservative stability conditions are derived with the introduction of new delay-interval-dependent terms and the exploitation of the delay subintervals size. The proposed analysis considers that the delay derivative is either upper and lower bounded, bounded above only, or unbounded, i.e., no restrictions are cast upon the derivative. Numerical examples are provided to enlighten the importance and advantages of the present criterion which outperforms previous criteria in time-delay systems literature. Also, an additional example is provided to highlight the effectiveness of the proposed H _∞ output tracking control design technique for complex nonlinear systems with time-varying delay.     

On Global Asymptotic Stability of Cohen–Grossberg Neural Networks With Variable Delays

This paper studies the problems of existence, uniqueness, global asymptotic stability and global exponential stability of the equilibrium of Cohen-Grossberg neural networks with variable delays. An estimation technique based on delay differential inequality with variable coefficients is developed to establish delay-independent/delay-dependent sufficient conditions for global asymptotic/exponential stability. The stability criteria obtained are based on the $M$-matrix theory. These criteria can be easily checked in practice and do not require that the delays be constant or differentiable. In particular, our delay-independent asymptotic/exponential stability criteria remove a restriction on the amplification functions imposed by the existing results. Furthermore, our delay-dependent exponential stability criteria give explicitly the allowable upper bound on the diagonal delays such that the global stability property of Cohen-Grossberg neural networks can be retained. Thus, our new results are of great importance in design and application of Cohen-Grossberg neural networks with variable delays. The effectiveness of the new results is further illustrated by two numerical examples in comparison with the existing results.      

基于LMI的时变时滞Cohen-Grossberg神经网络鲁棒稳定性

 研究了时变时滞Cohen-Grossberg神经网络的全局鲁棒稳定性问题.基于线性矩阵不等式技术,给出了保证时变时滞Cohen-Grossberg神经网络平衡点唯一性和全局鲁棒稳定性的新判据.这些新判据不依赖于时滞的大小和放大函数,且与现有的一些结果相比,具有易于验证、适用范围广、条件更不保守等特点.仿真结果验证了本文方法的有效性.     

Stability Criteria With Less LMI Variables for Neural Networks With Time-Varying Delay

In this letter, simplified delay-dependent stability criteria for neural networks are derived by using a simple integral inequality. The results are in terms of linear matrix inequalities (LMIs) and turn out to be equivalent to some existing results but include less number of LMI variables. This implies that some redundant variables in the existing stability criteria can be removed while maintaining the efficiency of the stability conditions. With the present stability conditions, the computational burden is largely reduced. Numerical examples are given to verify the effectiveness of the proposed criteria.      

Performance of a class of parallel spread-spectrum code acquisitionschemes in the presence of data modulation

An acquisition procedure for data-modulated direct-sequence spread-spectrum systems is investigated. The correlation time is partitioned into subintervals, and the integration results in these subintervals are noncoherently combined for detection. The tradeoff between noncoherent combining loss and data modulation degradation guides the optimum choice of the number of subintervals. Two forms of data modulation are considered, namely, the alternate-data and random-data cases. The parallel acquisition schemes discussed allow multiple code-phase offsets to be examined at each test. The circular state diagram approach is used to analyze the performances of these schemes. The theory presented is valid for a class of such parallel schemes     

Delay-Dependent Exponential Stability for Uncertain Stochastic Hopfield Neural Networks With Time-Varying Delays

This paper provides new delay-dependent conditions that guarantee the robust exponential stability of stochastic Hopfield type neural networks with time-varying delays and parameter uncertainties. Both the cases of the time-varying delays which are differentiable and may not be differentiable are considered. The stability conditions are derived by using the recently developed free-weighting matrices technique and expressed in terms of linear matrix inequalities. Numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria. It is shown that the proposed stability results are less conservative than some previous ones in the literature.      

A simple expression for the optimization of spread-spectrum code acquisition detectors operating in the presence of carrier-frequency offset

In this letter, we present a simple expression for the optimization of the threshold detection performance for direct-sequence spread-spectrum code acquisition in the presence of carrier-frequency offset. The proposed scheme divides the total integration time into subintervals, and the results of the coherent integrations performed over these subintervals are noncoherently combined prior to detection. The proposed expression allows obtaining the optimum number of coherent-integration subintervals for a given total integration time.     

Delay-Dependent Exponential Stability of Neural Networks With Variable Delay: An LMI Approach

This brief focuses on the problem of delay-dependent stability analysis of neural networks with variable delay. Two types of variable delay are considered: one is differentiable and has bounded derivative; the other one is continuous and may vary very fast. By introducing a new type of Lyapunov–Krasovskii functional, new delay-dependent sufficient conditions for exponential stability of delayed neural networks are derived in terms of linear matrix inequalities. We also obtain delay-independent stability criteria. Two examples are presented which show our results are less conservative than the existing stability criteria.     

Reappraisal of the unconditional stability criteria for active 2-port networks in terms of S parameters

Many different sets of stability criteria have been published, some of which are incorrect and others unnecessarily complicated. A reappraisal of the problem shows that the simplest criteria sufficient for unconditional stability are K>1 and |?|<1.     

An analysis of global asymptotic stability of delayed Cohen-Grossberg neural networks via nonsmooth analysis

In this paper, using a method based on nonsmooth analysis and the Lyapunov method, several new sufficient conditions are derived to ensure existence and global asymptotic stability of the equilibrium point for delayed Cohen-Grossberg neural networks. The obtained criteria can be checked easily in practice and have a distinguished feature from previous studies, and our results do not need the smoothness of the behaved function, boundedness of the activation function and the symmetry of the connection matrices. Moreover, two examples are exploited to illustrate the effectiveness of the proposed criteria in comparison with some existing results.     

Time-Varying Delay Passivity Analysis in 4 GHz Antennas Array Design

In this paper, a new approach for synchronization of dynamical networks with time-delays is proposed. It is based on stability theory of coupled time-delayed dynamical systems. Some new criteria for stability analysis which ensure the synchronization of the networks are analytically derived. Conditions for synchronization, in the form of Linear Matrix Inequality, are established. They use the Lyapunov and Krasovskii stability theories. In this approach, parameter uncertainties are introduced in the network model. Numerical simulations show the efficiency of the proposed synchronization analysis. A network of 4-GHz smart antenna array is used and analyzed in some details. This array provides a control of the direction of the radiation pattern.     

具时延的细胞神经网络的全局渐近稳定性分析

本文通过Lyapunov泛函方法和新的不等式a2b(2a3+b3)/3,(a,b0)分析技巧,讨论了一类具时延的细胞神经网络DCNN全局渐近稳定性问题,得到了若干新的充分判据,这些判据可用于设计出各种全局稳定的网络。     

A Delay Decomposition Approach to Delay-Dependent Robust Passive Control for Takagi–Sugeno Fuzzy Nonlinear Systems

This paper is concerned with the problem of delay-dependent robust passive control for a fuzzy nonlinear system with time-varying delays. A Takagi–Sugeno fuzzy model approach is exploited to design a passive control for nonlinear systems with time-varying delay. By decomposing the delay interval into multiple equidistant subintervals, new Lyapunov–Krasovskii functionals (LKFs) are constructed on these intervals. Employing these new LKFs, a new robust passive control criterion is proposed in terms of linear matrix inequalities, which is dependent on the size of the time delay. We also design a state feedback controller that guarantees a robustly strictly passive closed-loop system for all admissible uncertainties. Finally, two numerical examples are given to illustrate the effectiveness of the developed techniques.     

Global asymptotic stability of a class of feedback neural networks with an application to optimization problems

By applying results from homotopy theory, new conditions are obtained for the existence and uniqueness of an equilibrium for a class of continuous-time feedback neural networks which contains the Hopfield model as a special case. Next, new criteria are established for the global asymptotic stability of the unique equilibrium of this class of neural networks by utilizing Lur'e-type Lyapunov functions and the stability theory for systems of differential inequalities. Several practical stability testing conditions are given. As a special case, criteria are derived for the global asymptotic stability of Hopfield neural networks. This is followed by a robustness analysis of the class of neural networks considered. The results obtained are then applied to an optimization problem.This work was supported in part by the National Science Foundation under Grant ECS 93-19352.     

Exponential Stability of Impulsive Delayed Linear Differential Equations

This brief considers the global exponential stability of impulsive delayed linear differential equations. By utilizing the Lyapunov function methods combined with the Razumikhin technique, several criteria on exponential stability are analytically derived, which are substantially an extension and a generalization of the corresponding results in recent literature. Compared with some existing works, a distinctive feature of this brief is to address exponential stability problems for any fixed-time delays. It is shown that the delayed linear differential equations can be globally exponentially stabilized by impulses even if it may be unstable itself. An example and its simulation are also given to illustrate the theoretic results.     

A unifying proof of global asymptotical stability of neural networks with delay

We present some new global stability results of neural networks with delay and show that these results generalize recently published stability results. In particular, several different stability conditions in the literature which were proved using different Lyapunov functionals are generalized and unified by proving them using the same Lyapunov functional. We also show that under certain conditions, reversing the directions of the coupling between neurons preserves the global asymptotical stability of the neural network.     

Contact control concepts in manipulation robotics-an overview

This paper presents the state of the art in the control of robotic manipulators in constrained motion tasks. Contact control concepts are classified using different criteria, and their main characteristics are analyzed. For each of the presented contact control concepts, the essential characteristics are stated. The paper covers some early ideas and their later improvements, as well as trends in this field. The advantages and drawbacks of the various control schemes are outlined, and they are compared from the standpoint of their implementation issues. In the paper, all characteristic results in the stability analysis of robotic manipulators in the constrained motion tasks are briefly reported. A new approach to the correct solution of contact tasks control is mentioned as well     

高阶多智能体系统的一致性分析

 移动多智能体系统的一致性问题是复杂动力学系统中非常有现实意义的问题.在网络通信拓扑固定时,本文基于矩阵分解理论,提出了高阶多智能体系统中的信息流一致性收敛判据,给出了渐近收敛的充分必要条件;在通信拓扑具有最小生成树的前提下,通过构建多输入多输出系统的闭环传递函数,得到了各个阶次的状态量权重系数对收敛稳定性的影响效果,指出只要状态量权重满足Hurwitz稳定条件,即可实现一致性收敛.最后给出数值算例,仿真结果验证了结论的正确性.     

一种用于近场成像的修正广义相干处理方法

本文提出一种适用于近场转台成像的修正广义相干处理法，即对每一小转角内的距离－多普勒成像结果进行近场修正，从而实现近场大转角成像的方法，与球面反投影法相比，该方法计算量少，成像质量好。     

LMI Conditions for Robust Stability Analysis of?Stochastic Hopfield Neural Networks with Interval Time-Varying Delays and Linear Fractional Uncertainties

In this paper, the delay-dependent robust stability for a class of stochastic neural networks with linear fractional uncertainties is studied. The time-varying delay is assumed to belong to an interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the Lyapunov–Krasovskii functional, stochastic stability theory and some inequality techniques, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). In order to derive less conservative results, few free-weighting matrices are introduced. Three numerical examples are presented to show the effectiveness and improvement of the proposed method.