Robust H_∞ Control of Uncertain Linear Systems with Multiple State Delays—LMI Approach

This paper deals with the problem of robust H∞ control for uncertain systems with multiple state delays. The parameter uncertainties are unknown but norm - bounded. An LMI based method is developed for robust H∞ stabilization for a class of uncertain systems with multiple state delays via linear memoryless state feedback control. The resuits depend on the size of the delays and are given in terms of linear matrix inequalities, so they are less con. servative than those of delay - independent.     

Robust Controller Design of a Class of Uncertain Switched Fuzzy Systems with Delays

The robust control problem for a class of uncertain switched fuzzy systems with delays is investigated. Firstly,the model of the switched fuzzy system is presented and the parallel distributed compensation（ PDC） technology is employed to design fuzzy controllers. Then, based on the convex combination method, a sufficient condition for robust stabilization in terms of linear matrix inequalities（ LMIs） is obtained and a switching law is presented.Meanwhile,the Lyapunov-Krasovskii functional is taken to deal with time varying delays. Moreover,an algorithm is applied to finding a solution for a group of convex combination coefficient. Finally,a numerical example is given to demonstrate the effectiveness of the proposed method.     

Robust exponential control of a class of systems with uncertainties

The robust exponential stabilization problem for uncertain systems isstudied. Based on the solution for a nominal linear quadratic regulator problem with a prescribed degree of stability, the methods of constructing state feedback controllers are developed to ensure the robust stability of the closed loop system under the conditions weaker than the matching condition. Also, the cases where the matching condition is satisfied are considered in detail. Some examples are included to show the solution methods.     

Behaviors and high-order harmonic generation for short-range model potential in the intense laser field by using the symplectic algorithm

The asymptotic boundary condition to solve the time-dependent Schr dinger equation for a short-range model potential in an- intense laser field is presented. The condition is obtained by means of the asymptotic behavior of the short-rang emodel in the sufficiently large distance and Fourier transformation, and then the time-dependent Schr dinger equation is discretized into an inhomogeneous linear canonical equation. The inhomogeneous linear canonical equation is solved using the symplectic algorithm. The wavefunctions,time-calculatedevolution of the population on the bound state, and the high-orderharmonic generation verify that our numerical method is reasonable and effective.     

Robust Stabilization of Delayed Systems with Rank-1 Type Uncertainties

This paper deals with the problem of robust stabilizationfor the systems with rank-1 type uncertainties and mul-tiple state delay.Firstly,a new delay-dependent crite-ria is presented for unforced normal delayed system to bestable.Based on the obtained criteria,a new LMI ap-proach is developed for robust stabilization of a class ofuncertain delay systems via linear memoryless state feed-back control law.The results depend on the size of thedelays and are given in terms of linear matrix inequali-ties.And so no tuning procedure for scalars and/or ma-trices is required and the results are less conservativethan those of delay-independent.Finally,an illustra-tive example is presented.     

Robust H∞ Filter Design for a Class of Uncertain Linear Systems with Time—delayed Measurement

A robust H∞ approach to filtering for a class of uncertain linear systems with time-delayed measurement is investigated.The parameter uncertainties are described by real time-varying norm-bounded form.A methodology is developed for designing linear filters such that the filtering process remains robustly stable and the transfer function from the disturbance inputs to error state outputs meets the prescribed H∞-bound constraints.The filter can be obtained and parameterized by solving two Riccati equations based on the lemma of robust stability for control system with delay.Simulation example proves the method to be effective and feasible.     

Consensus of Second-Order Multi-Agent Systems with Time-Varying Delays and Antagonistic Interactions

This study investigates the consensus problem of second-order multi-agent systems subject to timevarying interval-like delays. The notion of consensus is extended to networks containing antagonistic interactions modeled by negative weights on the communication graph. A unified framework is established to address both the stationary and dynamic consensus issues in sampled-data settings. Using the reciprocally convex approach,a sufficient condition for consensus is derived in terms of matrix inequalities. Numerical examples are provided to illustrate the effectiveness of the proposed result.     

Delay-Dependent Exponential Stability of Stochastic Delayed Recurrent Neural Networks with Markovian Switching

The exponential stability problem is investigated for a class of stochastic recurrent neural networks with time delay and Markovian switching. By using Ito＇s differential formula and the Lyapunov stability theory, sufficient condition for the solvability of this problem is derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example and the simulation are exploited to demonstrate the effectiveness of the proposed results.     

A self-adaptive synchronization controller for Liu chaotic systems

A kind of synchronization controller for Liu chaotic systems whose nonlinear components are subject to Lipschitz condition was proposed. By using Lyapunov function and linear matrix inequality technique, a selfadaptive synchronization controller was constructed for Liu chaotic systems. There are two components of our derived synchronization controller: linear and nonlinear component. Linear component is composed of errors of the state variables between drivingsystems and respondingstems, and nonlinear component is a selfadaptive synchronization controller. a proof was given for proving the feasibility of this method, and numerical simulations of Liu chaotic systems show its effectiveness. Furthermore, this method can be applied to other chaotic systems, such as Chen systems, Lorenz systems, Chua systems and Rssler systems,etc.     

Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

By constructing a mcan-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts： a real-valued non-random recurrence equation and the standard Kalman filtering.     

ApcD is required for state transition but not involved in blue-light induced quenching in the cyanobacterium <Emphasis Type="Italic">Anabaena</Emphasis> sp. PCC7120

Pbycobilisomes （PBS） are able to transfer absorbed energy to photosystem Ⅰ and Ⅱ, and the distribution of light energy between two photosystems is regulated by state transitions. In this study we show that energy transfer from PBS to photosystem Ⅰ （PSI） requires ApcD. Cells were unable to perform state transitions in the absence of ApcD. The apcD mutant grows more slowly in light mainly absorbed by PBS, indicating that ApcD-dependent energy transfer to PSI is required for optimal growth under this condition. The apcD mutant showed normal blue-light induced quenching, suggesting that ApcD is not required for this process and state transitions are independent of blue-light induced quenching. Under nitrogen fixing condition, the growth rates of the wild type and the mutant were the same, indicating that energy transfer from PBS to PSI in heterocysts was not required for nitrogen fixation.     

ApcD is required for state transition but not involved in blue-light induced quenching in the cyanobacterium Anabaena sp.PCC7120

Pbycobilisomes(PBS) are able to transfer absorbed energy to photosystem I and Ⅱ,and the distribution of light energy between two photosystems is regulated by state transitions.In this study we show that energy transfer from PBS to photosystem I(PSI) requires ApcD.Cells were unable to perform state transitions in the absence of ApcD.The apcD mutant grows more slowly in light mainly absorbed by PBS,indicating that ApcD-dependent energy transfer to PSI is required for optimal growth under this condition.The apcD mutant showed normal blue-light induced quenching,suggesting that ApcD is not required for this process and state transitions are independent of blue-light induced quenching.Under nitrogen fixing condition,the growth rates of the wild type and the mutant were the same,indicating that energy transfer from PBS to PSI in heterocysts was not required for nitrogen fixation.     

Reliability analysis of M/G/1 queues with general retrial times and server breakdowns

This paper concerns the reliability issues as well as queueing analysis of M/G/1 retrial queues with general retrial times and server subject to breakdowns and repairs. We assume that the server is unreliable and customers who find the server busy or down are queued in the retrial orbit in accordance with a first-come-first-served discipline. Only the customer at the head of the orbit queue is allowed for access to the server. The necessary and sufficient condition for the system to be stable is given. Using a supplementary variable method, we obtain the Laplace-Stieltjes transform of the reliability function of the server and a steady state solution for both queueing and reliability measures of interest. Some main reliability indexes, such as the availability, failure frequency, and the reliability function of the server, are obtained.     

Reliability analysis of M/G/1 queues with general retrial times and server breakdowns

This paper concerns the reliability issues as well as queueing analysis of M/G/1 retrial queues with general retrial times and server subject to breakdowns and repairs. We assume that the server is unreliable and customers who find the server busy or down are queued in the retrial orbit in accordance with a first-come-first-served discipline. Only the customer at the head of the orbit queue is allowed for access to the server. The necessary and sufficient condition for the system to be stable is given. Using a supplementary variable method, we obtain the Laplace-Stieltjes transform of the reliability function of the server and a steady state solution for both queueing and reliability measures of interest. Some main reliability indexes， such as the availability, failure frequency, and the reliability function of the server， are obtained.     

Reliability analysis of M/G/1 queues with general retrial times and server breakdowns

This paper concerns the reliability issues as well as queueing analysis of M/G/1 retrial queues with general retrial times and server subject to breakdowns and repairs. We assume that the server is unreliable and customers who find the server busy or down are queued in the retrial orbit in accordance with a first-come-first-served discipline. Only the customer at the head of the orbit queue is allowed for access to the server. The necessary and sufficient condition for the system to be stable is given. Using a supplementary variable method, we obtain the Laplace-Stieltjes transform of the reliability function of the server and a steady state solution for both queueing and reliability measures of interest. Some main reliability indexes， such as the availability, failure frequency, and the reliability function of the server， are obtained.     

A new learning statistic for adaptive filter based on predicted residuals

A key problem for an adaptive filter is to establish a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information from some kinematic models. The reasonable adaptive factor needs a reliable learning statistics to judge the state kinematic model errors. After analyzing the existing two kinds of learning statistics based on the state discrepancy and variance component ratio, a new learning statistic based on predicted residuals is set up, which is different from the exiting learning statistics. The new learning statistic does not need to estimate the kinemetic state parameters before the filtering process, Of course, it does not need necessary measurements to estimate state parameters for all observation epochs. The new learning statistic can be applied together with the learning factor constructed by the state discrepancy. The advantages and shortcomings of the new learning factor are analyzed, and an example is given.     

Stability Analysis of Nonlinear Networked Control System with Integral Quadratic Constraints Performance in Takagi-Sugeno Fuzzy Model

This paper focuses on the stability analysis of nonlinear networked control system with integral quadratic constraints(IQC) performance, dynamic quantization, variable sampling intervals, and communication delays. By using input-delay and parallel distributed compensation(PDC) techniques, we establish the Takagi-Sugeno(T-S) fuzzy model for the system, in which the sampling period of the sampler and signal transmission delay are transformed to the refreshing interval of a zero-order holder(ZOH). By the appropriate Lyapunov-Krasovskii-based methods, a delay-dependent criterion is derived to ensure the asymptotic stability for the system with IQC performance via the H_∞ state feedback control. The efficiency of the method is illustrated on a simulation exampler.     

An Optimization Method of Stochastic Variable to Solve Uncertain Models in Engineering Design

The radical concepts of engineering stochastic optimization are introduced and a method for solving uncertain problems is developed according to the satisfied estimation of probabilistic values to stochastic constraints and the use of a quasi-gradient searching direction. The method is applied to engineering designs with linear or nonlinear stochastic constraints.