Optimal partitioned filter of stochastic distributed parameter dynamical systems with unknown initial state

1983 ◽  
Vol 315 (5-6) ◽  
pp. 347-385 ◽  
Author(s):  
Keigo Watanabe
Keyword(s):  
Dynamical Systems ◽  
Distributed Parameter ◽  
Initial State ◽  
Unknown Initial State

scholarly journals Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2471
Author(s):  
Tommaso Bradde ◽  
Samuel Chevalier ◽  
Marco De Stefano ◽  
Stefano Grivet-Talocia ◽  
Luca Daniel
Keyword(s):  
Dynamical Systems ◽  
Power System ◽  
System Dynamics ◽  
Real Time ◽  
Initial Conditions ◽  
Test System ◽  
Initial State ◽  
Power System Dynamics ◽  
Vector Fitting ◽  
Time Modeling

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suitable for online modeling of dynamical systems which experience both initial-state decay contributions in the measured output signals and concurrently active input signals. These adaptations were specifically contrived to meet the needs currently present in the electrical power systems community, where real-time modeling of low frequency power system dynamics is becoming an increasingly coveted tool by power system operators. After introducing and validating the RTVF scheme on synthetic test cases, this paper presents a series of numerical tests on high-order closed-loop generator systems in the IEEE 39-bus test system.

Oscillations-Induced Transitions and Their Application in Control of Dynamical Systems

1990 ◽  
Vol 112 (3) ◽  
pp. 313-319 ◽  
Author(s):  
J. Bentsman
Keyword(s):  
Dynamical Systems ◽  
Parabolic Equations ◽  
Closed Loop ◽  
Distributed Parameter ◽  
Control Laws ◽  
Loop Control ◽  
Finite Dimensional ◽  
Analytical Tools ◽  
Semilinear Parabolic ◽  
Qualitative Changes

Studies of the use of oscillations for control purposes continue to reveal new practically important properties unique to the oscillatory open and closed loop control laws. The goal of this paper is to enlarge the available set of analytical tools for such studies by introducing a method of analysis of the qualitative changes in the behavior of dynamical systems caused by the zero mean parametric excitations. After summarizing and slightly refining a technique developed previously for the finite dimensional nonlinear systems, we consider an extension of this technique to a class of distributed parameter systems (DPS) governed by semilinear parabolic equations. The technique presented is illustrated by several examples.

On the Almost Sure Stability of Linear Stochastic Distributed-Parameter Dynamical Systems

1966 ◽  
Vol 33 (1) ◽  
pp. 182-186 ◽  
Author(s):  
P. K. C. Wang
Keyword(s):  
Dynamical Systems ◽  
Asymptotic Stability ◽  
Integral Equations ◽  
Sufficient Conditions ◽  
Distributed Parameter ◽  
Almost Sure Stability ◽  
Partial Differential ◽  
Stochastic Parameters

In this paper, sufficient conditions for almost sure stability and asymptotic stability of certain classes of linear stochastic distributed-parameter dynamical systems are derived. These systems are described by a set of linear partial differential or differential-integral equations with stochastic parameters. Various examples are given to illustrate the application of the main results.

CNN: A Vision of Complexity

1997 ◽  
Vol 07 (10) ◽  
pp. 2219-2425 ◽  
Author(s):  
Leon O. Chua
Keyword(s):  
Dynamical Systems ◽  
Cellular Automata ◽  
Real Numbers ◽  
Initial State ◽  
Brain Science ◽  
Von Neumann ◽  
Local Activity ◽  
Sphere Of Influence ◽  
Nonlinear Pde’S ◽  
Special Case

CNN is an acronym for either Cellular Neural Network when used in the context of brain science, or Cellular Nonlinear Network when used in the context of coupled dynamical systems. A CNN is defined by two mathematical constructs: 1. A spatially discrete collection of continuous nonlinear dynamical systems called cells, where information can be encrypted into each cell via three independent variables called input, threshold, and initial state. 2. A coupling law relating one or more relevant variables of each cell Cij to all neighbor cells Ckl located within a prescribed sphere of influence Sij(r) of radius r, centered at Cij. In the special case where the CNN consists of a homogeneous array, and where its cells have no inputs, no thresholds, and no outputs, and where the sphere of influence extends only to the nearest neighbors (i.e. r = 1), the CNN reduces to the familiar concept of a nonlinear lattice. The bulk of this three-part exposition is devoted to the standard CNN equation [Formula: see text] where xij, yij, uij and zij are scalars called state, output, input, and threshold of cell Cij; akl and bkl are scalars called synaptic weights, and Sij(r) is the sphere of influence of radius r. In the special case where r = 1, a standard CNN is uniquely defined by a string of "19" real numbers (a uniform thresholdzkl = z, nine feedback synaptic weights akl, and nine control synaptic weights bkl) called a CNN gene because it completely determines the properties of the CNN. The universe of all CNN genes is called the CNN genome. Many applications from image processing, pattern recognition, and brain science can be easily implemented by a CNN "program" defined by a string of CNN genes called a CNN chromosome. The first new result presented in this exposition asserts that every Boolean function of the neighboring-cell inputs can be explicitly synthesized by a CNN chromosome. This general theorem implies that every cellular automata (with binary states) is a CNN chromosome. In particular, a constructive proof is given which shows that the game-of-life cellular automata can be realized by a CNN chromosome made of only three CNN genes. Consequently, this "game-of-life" CNN chromosome is a universal Turing machine, and is capable of self-replication in the Von Neumann sense [Berlekamp et al., 1982]. One of the new concepts presented in this exposition is that of a generalized cellular automata (GCA), which is outside the framework of classic cellular (Von Neumann) automata because it cannot be defined by local rules: It is simply defined by iterating a CNN gene, or chromosome, in a "CNN DO LOOP". This new class of generalized cellular automata includes not only global Boolean maps, but also continuum-state cellular automata where the initial state configuration and its iterates are real numbers, not just a finite number of states as in classical (von Neumann) cellular automata. Another new result reported in this exposition is the successful implementation of an analog input analog output CNN universal machine, called a CNN universal chip, on a single silicon chip. This chip is a complete dynamic array stored-program computer where a CNN chromosome (i.e. a CNN algorithm or flow chart) can be programmed and executed on the chip at an extremely high speed of 1 Tera (1012) analog instructions per second (based on a 100 × 100 chip). The CNN universal chip is based entirely on nonlinear dynamics and therefore differs from a digital computer in its fundamental operating principles. Part II of this exposition is devoted to the important subclass of autonomous CNNs where the cells have no inputs. This class of CNNs can exhibit a great variety of complex phenomena, including pattern formation, Turing patterns, knots, auto waves, spiral waves, scroll waves, and spatiotemporal chaos. It provides a unified paradigm for complexity, as well as an alternative paradigm for simulating nonlinear partial differential equations (PDE's). In this context, rather than regarding the autonomous CNN as an approximation of nonlinear PDE's, we advocate the more provocative point of view that nonlinear PDE's are merely idealizations of CNNs, because while nonlinear PDE's can be regarded as a limiting form of autonomous CNNs, only a small class of CNNs has a limiting PDE representation. Part III of this exposition is rather short but no less significant. It contains in fact the potentially most important original results of this exposition. In particular, it asserts that all of the phenomena described in the complexity literature under various names and headings (e.g. synergetics, dissipative structures, self-organization, cooperative and competitive phenomena, far-from-thermodynamic equilibrium phenomena, edge of chaos, etc.) are merely qualitative manifestations of a more fundamental and quantitative principle called the local activity dogma. It is quantitative in the sense that it not only has a precise definition but can also be explicitly tested by computing whether a certain explicitly defined expression derived from the CNN paradigm can assume a negative value or not. Stated in words, the local activity dogma asserts that in order for a system or model to exhibit any form of complexity, such as those cited above, the associated CNN parameters must be chosen so that either the cells or their couplings are locally active.

scholarly journals A New Type of Distributed Parameter Control Systems: Two-Point Boundary Value Problems for Infinite-Dimensional Dynamical Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-3
Author(s):  
De-Xing Kong ◽  
Fa Wu
Keyword(s):  
Dynamical Systems ◽  
Boundary Value Problems ◽  
Control Systems ◽  
Distributed Parameter ◽  
Parameter Control ◽  
Point Boundary ◽  
Infinite Dimensional ◽  
Infinite Dimensional Dynamical Systems ◽  
Distributed Parameter Control ◽  
New Type

This survey note describes a new type of distributed parameter control systems—the two-point boundary value problems for infinite-dimensional dynamical systems, particularly, for hyperbolic systems of partial differential equations of second order, some of the discoveries that have been done about it and some unresolved questions.

scholarly journals Quasistatic dynamical systems

2016 ◽  
Vol 37 (8) ◽  
pp. 2556-2596 ◽  
Author(s):  
NEIL DOBBS ◽  
MIKKO STENLUND
Keyword(s):  
Dynamical System ◽  
Dynamical Systems ◽  
Time Evolution ◽  
Chaotic Maps ◽  
Martingale Problem ◽  
Initial Distribution ◽  
Initial State ◽  
Statistical Behavior ◽  
Long Time ◽  
Well Posed

We introduce the notion of a quasistatic dynamical system, which generalizes that of an ordinary dynamical system. Quasistatic dynamical systems are inspired by the namesake processes in thermodynamics, which are idealized processes where the observed system transforms (infinitesimally) slowly due to external influence, tracing out a continuous path of thermodynamic equilibria over an (infinitely) long time span. Time evolution of states under a quasistatic dynamical system is entirely deterministic, but choosing the initial state randomly renders the process a stochastic one. In the prototypical setting where the time evolution is specified by strongly chaotic maps on the circle, we obtain a description of the statistical behavior as a stochastic diffusion process, under surprisingly mild conditions on the initial distribution, by solving a well-posed martingale problem. We also consider various admissible ways of centering the process, with the curious conclusion that the ‘obvious’ centering suggested by the initial distribution sometimes fails to yield the expected diffusion.

Sign in / Sign up

Export Citation Format

Share Document