Balanced realisations and model reduction of periodicallytime-varying state-space digital filters

The authors discuss balanced realisations and model reduction of periodically time-varying (PTV) state-space digital filters. Controllability and observability Grammians of PTV state-space digital filters are discussed. It is extremely interesting to notice that although PTV state-space digital filters can be implemented by using a group of time-invariant coefficient sets, controllability and observability Grammians cannot be evaluated independently by using any one set of these time-invariant coefficients. Also, important physical interpretations of controllability and observability Grammians are considered. Based on these analysis results, balanced realisations of PTV state-space digital filters are defined and a synthesis method for balanced realisations is proposed. As one application of balanced realisations, a reduced-order model of a PTV state-space digital filter can be obtained by taking a subsystem of balanced realisation of the PTV state-space digital filter. Finally, a numerical example is given to illustrate the balanced model reduction procedure     

Small-signal modeling of pulse-width modulated switched-mode powerconverters

The state-space averaging method, associated with power converters, is discussed, Emphasis is on the motivation and objectives of modeling by equivalent circuits based on physical interpretation of state-space averaging. The discussion is limited to pulse-width-modulated DC-to-DC converters     

Two's complement quantization in two-dimensional state-spacedigital filters

Two-dimensional (2-D) state-space digital filters are investigated for stability with and without quantization. A sufficient condition is obtained for the stability of the linear Fornasini-Marchesini (1976) state-space model. The same model is then studied for stability when implemented using two's complement truncation quantization, and a sufficient condition for asymptotic stability of the nonlinear filter is obtained. In the process, a theorem is proved which gives a sufficient condition for the stability of a one-dimensional (1-D) state-space digital filter under the same type of quantization     

Modification of Schwarz approximation

The state-space Schwarz approximation method is modified so that the original and the derived reduced model have an identical combination of their first time moments and Markov parameters. The proposed modification is in fact a state-space formulation of a frequency-domain method, which has appeared in previous publications.     

Small signal analysis of the LCC-type parallel resonant converterusing discrete time domain modeling

Discrete state-space modeling of the LCC-type parallel resonant power converter is presented. Using these large signal equations, small signal modeling of the power converter is obtained. Multiple loops have been used for the closed loop operation. State variable feedback control has been integrated with the linear small signal state-space model and the associated control aspects are studied. The small signal state-space model has been used to study the small signal behavior of the power converter for open loop and closed loop operation for parameters like control to output transfer function, audio-susceptibility and output impedance. Key theoretical results have been experimentally verified     

基于自适应Kalman滤波的二维有噪子带信号恢复

基于子带信号的多通道表示（multichannel representation)和输入信号的动态特征,本文尝试推出了一种多分辨率状态空间模型,它与带相加子带噪声的滤波器组（Filter Bank)系统是等价的,于是使有噪子带信号的恢复可表述为相应多分辨率态空间模型的最优状态估计问题。进一步又利用信号的向量动态模型,发展了适于二维Kalman滤波的二维多分辨率状态空间模型,根据信号行为的分布,目标平面（object plane)可分割为不同的区域并用不同的向量动态模型来表征信号的非平衡分布,计算机数字仿真结果进一步证实了本文所提出了二维多分辨率Kalman滤波器性能的优越性。     

Spectral factorization of time-varying covariance functions

The determination of the state-space equations of a time-varying finite-dimensional linear system with a prescribed output covariance matrix is considered when the system is excited by Gaussian white-noise inputs. It is shown that a symmetric state covariance matrix provides the key link between the state-space equations of a system and the system output covariance matrix. Furthermore, such a matrix satisfies a linear matrix differential equation if the state-space equations of the system are known, and a matrix Riccati equation if the output covariance matrix of the system is given. Existence results are given for the Riccati equation solution, and discussion of asymptotic solutions of the differential equations is also included.     

一般二维数字系统的低噪声优化实现

本文给出了一般二维数字系统的两种高效率状态空间结构;导出了这两种结构的能控性和能观性格拉姆矩阵的有关性质;建立了二维数字系统定点运算的全局噪声模型;据此获得了一般二维数字系统低噪声、高效率的优化实现,文中举例说明了本文的两种结构的噪声性能和计算效率各不相同。     

Joint Estimation of States and Parameters for an Input Nonlinear State-Space System with Colored Noise Using the Filtering Technique

This paper concerns the state and parameter estimation problem for an input nonlinear state-space system with colored noise. By using the data filtering and the over-parameterization technique, we transform the original nonlinear state-space system into two identification models with filtered states: one containing the system parameters and the other containing the noise model’s parameters. A combined state and parameter estimation algorithm is developed for identifying the state-space system. The key is that the estimation of system parameters uses the estimated states, and the estimation of states uses the preceding parameter estimates. A simulation example is provided to show that the proposed algorithm can work well.     

Optimal signal reconstruction in noisy filter bank systems:multirate Kalman synthesis filtering approach

A multirate Kalman synthesis filter is proposed in this paper to replace the conventional synthesis filters in a noisy filter bank system to achieve optimal reconstruction of the input signal. Based on an equivalent block representation of subband signals, a state-space model is introduced for an M-band filter bank system with subband noises. The composite effect of the input signal, analysis filter bank, decimators, and interpolators is represented by a multirate state-space model. The input signal is embedded in the state vector, and the corrupting noises in subband paths are generally considered as additive noises. Hence, the signal reconstruction problem in the M-band filter bank systems with subband noises becomes a state estimation procedure in the resultant multirate state-space model. The multirate Kalman filtering algorithm is then derived according to the multirate state-space model to achieve optimal signal reconstruction in noisy filter bank systems. Based on the optimal state estimation theory, the proposed multirate Kalman synthesis filter provides the minimum-variance reconstruction of the input signal. Two numerical examples are also included. The simulation results indicate that the performance improvement of signal reconstruction in noisy filter bank systems is remarkable     

State-Space Self-Tuning Control for Stochastic Fractional-Order Chaotic Systems

Based on the modified state-space self-tuning control (STC), a novel low-order tuner via the modified observer/Kalman filter identification (OKID) is proposed for stochastic fractional-order chaotic systems. The OKID method is a time-domain technique that identifies a discrete input-output map by using known input-output sampled data in the general coordinate form, through an extension of the eigensystem realization algorithm (ERA). First, the estimated system in the general coordinate based on the conventional OKID method is transformed to the one in an observer form to fit the state-space innovation form for the STC. Then, in stead of the conventional recursive least squares (RLS) identification algorithm used for STC, the Kalman filter as a parameter estimator with the state-space innovation form is presented for effectively estimating the time-varying parameters. Besides, taking the advantage of the digital redesign approach, the derivation of the current-output-based observer is proposed for the modified STC. As a result, the low-order state-space self-tuner with the high-gain controller property is then proposed for stochastic fractional-order chaotic systems, which the fractional operators are well approximated using the standard high integer-order operators. Finally, the fractional-order Chen and Roumlssler systems with stochastic system process and measurement noises are used as illustrative examples to demonstrate the effectiveness of the proposed methodology     

Impact of Feedback Delay on Closed-Loop Stability in Semiconductor Optical Amplifier Control Circuits

Semiconductor optical amplifiers (SOAs) are attractive for integrated photonic signal processing, but because their response is so fast, delays in a controller feedback path can jeopardize performance and stability. Using state-space methods, we quantify the constraints imposed on feedback controllers by closed-loop delay. We first derive a complete nonlinear state-space control model of a SOA with an equivalent circuit containing parasitics and dynamic impedance; the analytical state-space model agrees well with a validated photonic-only control model. Using a linearized version of the model we demonstrate that time delay in the feedback path can destabilize the SOA through phase accumulation. We then apply linear system theory to calculate the best-case stable delay margin for a given controller norm, and find a potentially severe inverse relationship between delay margin and controller norm. Finally, guided by the delay-controller relationship we design a hybrid feedforward-feedback controller to illustrate that good transient and steady-state regulation is obtained by carefully balancing the feedforward and feedback components. Our state-space modeling and design methods are general and are easily adapted to the design and analysis of more complex photonic circuits.     

Simple state-space formulations of 2-D frequency transformation and double bilinear transformation

In this paper, an explicit relationship between the two-dimensional (2-D) frequency transformation and the theory of linear fractional transformation (LFT) representation is shown. Based on this relationship, a simple alternative state-space formulation of 2-D frequency transformation for 2-D digital filters is derived by utilizing the well-known Redheffer star product of LFT representations. The proposed formulation is then utilized to establish a simple relationship between the state-space representations of a 2-D continuous system and a 2-D discrete system which are related by the double bilinear transformation. Moreover, the inherent relations among the proposed formulations and the existing results are discussed. It turns out that all the existing results given in the literature can be unified as special or equivalent cases by the new state-space formulation of 2-D frequency transformation in a very concise and elegant form. Numerical examples are given to illustrate the effectiveness of the proposed formulations.     

Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation

A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the integro-difference equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model's spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples.     

Formal verification of timed systems: a survey and perspective

An overview of the current state of the art of formal verification of real-time systems is presented. We discuss commonly accepted models, specification languages, verification frameworks, state-space representation schemes, state-space construction procedures, reduction techniques, pioneering tools, and finally some new related issues. We also make a few comments according to our experience with verification tool design and implementation.     

Generalized averaging method for power conversion circuits

A more general averaging procedure that encompasses state-space averaging and that is potentially applicable to a much broader class of circuits and systems is presented. Examples of its application in resonant and PWM power convertors are presented. The technique is shown to be effective on a number of examples. including resonant type converters. The approach offers refinements to the theory of state-space averaging, permitting a framework for analysis and design when small ripple conditions do not hold. The method may find applications in simulation and design since it is considerably easier to simulate an averaged model than a switched model     

Digital Modeling and PID Controller Design for MIMO Analog Systems with Multiple Delays in States, Inputs and Outputs

This paper presents a discrete-time state-space methodology for the digital modeling and design of an optimal digital proportional-integral-derivative (PID) plus state-feedback controller for multiple-input, multiple-output (MIMO) continuous-time systems with multiple time delays in states, inputs and outputs. To implement the digital design, first the Chebyshev quadrature formula together with a linear interpolation method is employed to obtain an extended discrete-time state-space model from the continuous-time multiple time-delay system. Then, a partially predetermined digital PID controller and the extended discrete-time state-space model are formulated as an augmented discrete-time state-space system utilizing state-feedforward and state-feedback linear-quadratic regulator (LQR) design. As a result, the parameters of the optimal PID controller and its associated state-feedback controller can be determined by tuning the weighting matrices in the LQR performance criteria. Further, an optimal discrete-time observer is jointly constructed for the multivariable system with multiple delays in states, inputs and outputs. The proposed design methodology can be applied to general MIMO continuous-time multiple time-delay systems for performance improvement and disturbance rejection. An illustrative example is given to demonstrate the effectiveness of the developed method. This work was supported in part by U.S. Army Research Office under Grant W911NF-06-1-0507, the National Science Foundation under Grant NSF0717860, and Research Contract 1440234.     

An Efficient State-Space Realization With Minimum Roundoff Noise Gain

This paper deals with efficient digital filter structures with roundoff noise consideration. Motivated by the direct-form II transposed (DFIIt) structure in rho-operator (rhoDFIIt) an alternative structure is obtained [Li in 2005, where, instead of the first-order rho-operators in rhoDFIIt, a set of second-order polynomial operators is used. In this paper, with the rounding before multiplication implementation taken into account, the equivalent state-space realization of the proposed structure by Li is derived and its roundoff noise performance is analyzed by deriving the roundoff noise expressions without/with error feedback consideration. This state-space realization can be efficiently implemented and has more degrees of freedom than its counterpart rhoDFIIt, which can be utilized to minimize the roundoff noise gain. A genetic algorithm is proposed to efficiently solve the optimal structure problem. Extensive examples are given to illustrate the advantage of this state-space realization and support the theoretical analysis