In optical packet switches, the overhead of reconfiguring a switch fabric is not negligible with respect to the packet transmission time and can adversely affect switch performance. The overhead increases the average waiting time of packets and worsens throughput performance. Therefore, scheduling packets requires additional considerations on the reconfiguration frequency. This work intends to analytically find the optimal reconfiguration frequency that minimizes the average waiting time of packets. It proposes an analytical model to facilitate our analysis on reconfiguration optimization for input-buffered optical packet switches with the reconfiguration overhead. The analytical model is based on a Markovian analysis and is used to study the effects of various network parameters on the average waiting time of packets. Of particular interest is the derivation of closed-form equations that quantify the effects of the reconfiguration frequency on the average waiting time of packets. Quantitative examples are given to show that properly balancing the reconfiguration frequency can significantly reduce the average waiting time of packets. In the case of heavy traffic, the basic round-robin scheduling scheme with the optimal reconfiguration frequency can achieve as much as 30% reduction in the average waiting time of packets, when compared with the basic round-robin scheduling scheme with a fixed reconfiguration frequency.

This paper studies the problem of the relations between existence conditions of common quadratic and those of common infinity-norm Lyapunov functions for sets of discrete-time linear time-invariant (LTI) systems. Based on the equivalence between the robust stability of a class of time-varying systems and the existence of a common infinity-norm Lyapunov function for the corresponding set of LTI systems, the relations are determined. It turns out that although the relation is an equivalent one for single stable systems, the existence condition of common infinity-norm type is strictly implied by that of common quadratic type for the set of systems. Several existence conditions of a common infinity-norm Lyapunov functions are also presented for the purpose of easy checking.

A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.

This paper proposes new recursive fixed-point smoother and filter using covariance information in linear discrete-time stochastic systems. In this paper, to be able to treat the estimation of the stochastic signal, a performance criterion, extended from the criterion in the H estimation problem, is newly proposed. The criterion is transformed equivalently into a min-max principle in game theory, and an observation equation in a Krein space is obtained as a result. The estimation accuracy of the proposed estimators are compared with the recursive least-squares (RLS) Wiener estimators, the Kalman filter and the fixed-point smoother based on the state-space model.

This paper proposes a measure of coefficient quantization errors for linear discrete-time state-space systems. The proposed measure of state-space systems agrees with the actual output error variance since it is derived from the exact evaluation of the output error variance due to coefficient deviation. The measure in this paper is represented by the controllability and the observability gramians and the state covariance matrix of the system. When the variance of coefficient variations is very small, the proposed measure is identical to the conventional statistical sensitivity of state-space systems. This paper also proposes a method of synthesizing minimum measure structures. Numerical examples show that the proposed measure is in very good agreement with the actual output error variance, and that minimum measure structures have a very small degradation of the frequency characteristic due to coefficient quantization.

This paper provides a new robust guaranteed cost controller design method for discrete parameter uncertain time delay systems. The result shows much tighter bound of guaranteed cost than that of existing paper. In order to get the optimal (minimum) value of guaranteed cost, an optimization problem is given by linear matrix inequality (LMI) technique. Also, the parameter uncertain systems with time delays in both state and control input are considered.

The robust induced l-norm control problem is considered for uncertain discrete-time systems. We propose a state feedback and an output feedback controller that quadratically stabilize the systems and satisfy a given constraint on the induced l-norm. Both controllers are constructed by solving a set of scalar-dependent linear matrix inequalities (LMI's), and the gain matrices are characterized by the solution to the LMI's.

We present a minimal lattice realization of MIMO linear discrete-time systems which interpolate the desired Markov and covariance parameters. The minimal lattice realization is derived via a recursive construction algorithm based on the state space description and it parametrizes all the interpolants.

We present a recursive algorithm for constructing linear discrete-time systems which interpolate the desired 1st-and 2nd-order information. The recursive algorithm constructs a new system and connects it to the previous system in the cascade form every time new information is added. These procedures yield a practical realization of all the interpolants.

The recursive least-squares filter and fixed-point smoother are designed in linear discrete-time systems. The estimators require the information of the system matrix, the observation vector and the variances of the state and white Gaussian observation noise in the signal generating model. By appropriate choices of the observation vector and the state variables, the state-space model corresponding to the ARMA (autoregressive moving average) model of order (n,m) is introduced. Here,some elements of the system matrix consist of the AR parameters. This paper proposes modified iterative technique to the existing one regarding the estimation of the variance of observation noise based on the estimation methods of ARMA parameters in Refs. [2],[3]. As a result, the system matrix, the ARMA parameters and the variances of the state and observation noise are estimated from the observed value and its sampled autocovariance data of finite number. The input noise variance of the ARMA model is estimated by use of the autocovariance data and the estimates of the AR parameters and one MA parameter.