A discrete state-space model for linear image processing

The linear time-discrete state-space model is generalized from single-dimensional time to two-dimensional space. The generalization includes extending certain basic known concepts from one to two dimensions. These concepts include the general response formula, state-transition matrix, Cayley-Hamilton theorem, observability, and controllability.     

The determination of state-space representations for linear multivariable systems

A new, relatively simple, constructive method is presented for obtaining state-space or normal form representations for linear, time invariant systems whose dynamics are expressed in a more general matrix differential operator form. The development employed provides new insight into various structural properties of linear systems. Equivalence is defined for a rather large class of linear systems, and an algorithm is given for reducing any member of this class to normal form. In order to outline the algorithm, a number of “well known” results involving polynomial matrices are employed for the first time, along with the “structure theorem”. An example is used to illustrate the algorithm and two appendices are employed to clarify the development.     

On obtaining the coefficients of the output transfer function from a state-space model and an output model of a linear constant coefficient system

A compact procedure for the calculation of the coefficients of the output transfer function of a system is presented. The coefficients of the plant characteristic polynomial are obtained as an explicit byproduct of this approach. Some additional insight into the consequence of the system's state being unobservable by the output is gained. If the state is fully observable via the output, the characteristic polynomial of the system state transfer matrix is determined.     

A general algorithm for determining state-space representations

A new and direct procedure is presented for determining state-space representations of given, time-invariant systems whose dynamical behavior is expressed in a more general, differential operator form. The procedure employs some preliminary polynomial matrix operations, if necessary, in order to “reduce” the given system to an equivalent differential operator form which satisfies four specific conditions. An equivalent state-space representation is then determined in a most direct manner; i.e. the algorithm presented requires only a single matrix inversion. An explicit relationship between the partial state and input of the given system and the state of the equivalent state-space system is also obtained.     

Parallel state-space search for a first solution with consistent linear speedups

Consider the problem of exploring a large state-space for a goal state where although many such states may exist in the state-space, finding any one state satisfying the requirements is sufficient. All the methods known until now for conducting such search in parallel using multiprocessors fail to provide consistent linear speedups over sequential execution. The speedups vary between sublinear to superlinear and from one execution to another. Further, adding more processors may sometimes lead to a slow-down rather than speedup, giving rise to speedup anomalies reported in literature. We present a prioritizing strategy which yields consistent speedups that are close toP withP processors, and that monotonically increase with the additon of processors. This is achieved by keeping the total number of nodes expanded during parallel search very close to that of a sequential search. In addition, the strategy requires substantially smaller memory relative to other methods. The performance of this strategy is demonstrated on a multiprocessor with several state-space search problems.This research has been supported in part by the National Science Foundation under Contract No. CCR-89-02496.     

通用数字信号处理系统的实现

本文简述了PCL1800卡设备驱动程序的工作过程，介绍了基本开发软件包实现高速数据采集和模拟信号输出的程序编制方法，最后，给出了在Windows平台下使用多线程技术的通用信号处理系统实例，并给出了C 源代码。     

The general fault detection and isolation problem for 2D state-space models

In this paper, the fault detection and isolation problem for two-dimensional (2D) state-space models is investigated in a rather general setting, by assuming that disturbances may affect the system. In a recent contribution [M. Bisiacco, M.E. Valcher, Observer-based fault detection and isolation for 2D state-space models, Multidim. Systems Signal Process 17 (2006) 219–242], a complete theory of dead-beat (possibly unknown input) observer-based fault detectors and isolators (FDIs) has been developed. Here the wider class of 2D FDIs, which allow to detect and identify possible faults, without requiring a preliminary state estimation, is introduced, and necessary and sufficient conditions for the existence of a dead-beat FDI are derived. Constructive procedures for obtaining such FDIs, and comparisons with the results obtained in [M. Bisiacco, M.E. Valcher, Observer-based fault detection and isolation for 2D state-space models, Multidim. Systems Signal Process 17 (2006) 219–242], are also provided.     

Simultaneous observation of linear systems: a state-space interpretation

Necessary and sufficient conditions for the existence of a simultaneous functional observer for several linear time-invariant (LTI) systems are derived using distinguishability concepts, that generalize the detectability concept for a single system. They generalize also the known results. A structure for common functional observers is proposed, and for two systems, or for several systems without inputs, the problem can be constructively solved. A relationship between the simultaneous observation problem and the existence of unknown input observers is established.     

A state-space model for automatic instruction

This paper introduces a state-space instructional model, in which instruction is viewed as a process of transforming the student from one state to another. The model is expressed in terms of state-space problem solving as in artificial intelligence research, an area to which computer-assisted instruction has been increasingly turning for solutions to some of its fundamental problems. The model is first described in general terms and it is explained how the usual concerns of computer-assisted instruction (such as response-sensitivity and individualisation) find expression in the model. The use of the model in the design of teaching programs is illustrated by two systems, for paired-associate teaching and concept teaching. The former involves a re-expression of familiar methods based on the use of mathematical learning models; the latter uses more heuristic methods derived from artificial intelligence. Finally, some comparisons with other instructional models are made.     

Reduced transfer matrix from the more general 2D state-space model of Fornasini-Marchesini

Using a generalization of the Pad´ approximation method, an algorithm is presented for the transfer function of multidimensional systems described by the Fornasini and Marchesini state-space model.     

Iterative learning control for two-dimensional linear discrete systems with Fornasini-Marchesini model

International Journal of Control, Automation and Systems - Almost all existing iterative learning control (ILC) algorithms have focused on one-dimensional (1-D) dynamical systems, and seldom were...     

带线性输出的一般非线性系统的观测器设计

一般非线性系统的观测器设计一直是控制理论研究的重点.本文针对一类带线性输出的一般非线性系统的观测器设计问题,通过微分中值定理,给出了观测器的一般形式.然后利用构造“观测器Lyapunov函数”,提出了一种较为宽松的一般非线性系统观测器设计方法,并证明其渐进稳定性.针对两个典型的一般非线性系统和双线性系统的分析说明了所给算法是可行的.最后,通过对一个单摆系统的观测器构造方法可以看出该算法是简单的且是易于实现的.     

Uniquely identifiable state-space and ARMA parametrizations for multivariable linear systems

Multivariable systems can be represented, in a uniquely identifiable way, either by canonical forms or by so-called overlapping forms. The advantage of the latter is that they do not require the a priori estimation of a set of structural invariants (e.g. Kronecker invariants). We show here how to define uniquely identifiable overlapping parametrizations for state-space and ARMA models. We show that these parametrizations are all related to a set of intrinsic invariants, which are obtained from the Markov parameters of the system. Different forms of overlapping ARMA parametrizations are derived and their properties discussed.     

Recursive input-output and state-space solutions for continuous-time linear estimation problems

A general linear least-squares estimation problem is considered. It is shown how the optimal filters for filtering and smoothing can be recursively and efficiently calculated under certain structural assumptions about the covariance functions involved. This structure is related to an index known as the displacement rank, which is a measure of non-Toeplitzness of a covariance kernel. When a state space type structure is added, it is shown how the Chandrasekhar equations for determining the gain of the Kalman-Bucy filter can be derived directly from the covariance function information; thus we are able to imbed this class of state-space problems into a general input-output framework.     

Stability robustness bounds for linear state-space models with structured uncertainty

In this note, we consider the robust stability analysis problem in linear state-space models. We consider systems with structured uncertainty. Some lower bounds on allowable perturbations which maintain the stability of a nominally stable system are derived. These bounds are shown to be less conservative than the existing ones.     

基于状态空间平均模型的全数字双闭环斩波内馈串级调速系统

首先介绍了内馈斩波串级调速的工作原理,其次采用状态空间平均模型建立了内馈斩波串级调速系统的小信号模型,推导了斩波器双闭环控制系统的动态模型,并用DSP实现了全数字控制．斩波器电流内环控制,提高了系统的可靠性,改善了速度稳态精度和动态响应．本系统已运行于工业现场,节能效果显著．     

Feature detection using the general linear model

The detection of simple features using position-invariant filters constructed by the use of the matched filter theorem is extended to the general case of features with more than one free parameter.     

Estimating a state-space model from point process observations

A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.