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Results for “Stochastic Systems”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Model reduction for slow–fast stochastic systems with metastable behaviour

The quasi-steady-state approximation (or stochastic averaging principle) is a useful tool in the study of multiscale stochastic systems, giving a practical method by which to reduce the number of degrees of freedom in a model. The method is extended here to slow–fast systems in which the fast variables exhibit metastable behaviour. The key parameter that determines the form of the reduced model is the ratio of the timescale for the switching of the fast variables between metastable states to the timescale for the evolution of the slow variables. The method is illustrated with two examples: one from biochemistry (a fast-species-mediated chemical switch coupled to a slower varying species), and one from ecology (a predator–prey system). Numerical simulations of each model reduction are compared with those of the full system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

MATHEMATICAL FRAMEWORK FOR THE ANALYSIS OF DYNAMC STOCHASTIC SYSTEMS WITH THE RAVEN CODE

RAVEN (Reactor Analysis and Virtual control Environment) is a software code under development at Idaho National Laboratory aimed at performing probabilistic risk assessment and uncertainty quantification using RELAP-7, for which it acts also as a simulation controller. In this paper we will present the equations characterizing a dynamic stochastic system and we will then discuss the behavior of each stochastic term and how it is accounted for in the RAVEN software design. Moreover we will present preliminary results of the implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Numerical and Analytical Methods of Study of Stochastic Systems with Delay

This paper is a brief review of methods of qualitative and quantitative study of various classes of stochastic systems with aftereffect. We describe schemes of analysis of stochastic ordinary and partial differential and integrodifferential equations, which are also applicable for their deterministic analogs. These numerical and analytical schemes are implemented in the software package Mathematica and in the language Intel Fortran.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Linear regulator design for stochastic systems by a multiple time scales method

A hierarchically-structured, suboptimal controller for a linear stochastic system composed of fast and slow subsystems is considered. The controller is optimal in the limit as the separation of time scales of the subsystems becomes infinite. The methodology is illustrated by design of a controller to suppress the phugoid and short period modes of the longitudinal dynamics of the F-8 aircraft.

Teneketzis, D.↗
NASA NTRS1977

Control of nonlinear stochastic systems using adaptive estimation

A general procedure for the design of a digital controller for nonlinear stochastic systems is presented; the emphasis is on development of partitioned-adaptive control techniques which provide rapid tracking of the system state vector to follow rapid trajectory variations. Tests involving the control of a low-order nonlinear oscillator suggest that the procedure yields an effective method for achieving closed-loop performance, despite the difficulty of the control problem

Vanlandingham, H. F.↗
DOE OSTI2018

EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems

In this paper, a novel control algorithm is presented to enhance the performance of tracking property for a class of non-linear dynamic stochastic systems with unmeasurable variables. To minimize the entropy of tracking errors without changing the existing closed loop with PI controller, the enhanced performance loop is constructed based on the state estimation by extended Kalman Filter and the new controller is designed by full state feedback following this presented control algorithm. Besides, the conditions are obtained for the stability analysis in the mean square sense. In the end, the comparative simulation results are given to illustrate the effectiveness of proposed control algorithm.

Complex industrial process↗
DOE OSTI2017

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

Entropy↗
NASA NTRS1996

Indirect Identification of Linear Stochastic Systems with Known Feedback Dynamics

An algorithm is presented for identifying a state-space model of linear stochastic systems operating under known feedback controller. In this algorithm, only the reference input and output of closed-loop data are required. No feedback signal needs to be recorded. The overall closed-loop system dynamics is first identified. Then a recursive formulation is derived to compute the open-loop plant dynamics from the identified closed-loop system dynamics and known feedback controller dynamics. The controller can be a dynamic or constant-gain full-state feedback controller. Numerical simulations and test data of a highly unstable large-gap magnetic suspension system are presented to demonstrate the feasibility of this indirect identification method.

Huang, Jen-Kuang↗
NASA NTRS1976

Linear regulator design for stochastic systems by a multiple time scales method

This paper develops a hierarchically-structured, suboptimal controller for a linear stochastic system composed of fast and slow subsystems. It is proved that the controller is optimal in the limit as the separation of time scales of the subsystems becomes infinite. The methodology is illustrated by design of a controller to suppress the phugoid and short period modes of the longitudinal dynamics of the F-8 aircraft.

Teneketzis, D.↗
NASA NTRS1977

Linear regulator design for stochastic systems by a multiple time-scales method

This short paper develops a hierarchically structured, suboptimal controller for a linear stochastic system composed of fast and slow subsystems. It is proved that the controller is optimal in the limit as the separation of time scales of the subsystems becomes infinite. The methodology is illustrated by design of a controller to suppress the phugoid and short-period modes of the longitudinal dynamics of the F-8 aircraft.

Teneketzis, D.↗
NASA NTRS1975

Connective stability of large-scale stochastic systems

The procedure of decomposition-aggregation analysis reported by Siljak (1973) and Grujic and Siljak (1973) has been used in a study of the connective-stability aspects of large-scale stochastic systems. An investigation is conducted of the tolerance of a system of interconnected deterministic subsystems to both deterministic and stochastic interactions. The connective stability in the mean is considered and the connective property of stability is defined. The defined concept is included in a modification of the comparison theorem described by Ladde (1975). Attention is also given to a derivation of the sufficient conditions for the connective stability in the mean.

Ladde, G. S.↗
NASA NTRS1987

State-space self-tuning controllers for general multivariable stochastic systems

This paper presents a state-space approach for self-tuning control of a more general class of multivariable stochastic systems having a number of inputs equal or different from the number of outputs. The dynamic system is represented in the state-space innovation form with Luenberger's canonical structures. The model parameters and the Kalman gain are identified via either the extended least-squares algorithm or the least-squares ladder algorithm. The Kalman gain matrix and states can be estimated from the identified parameters without utilizing the standard state estimation algorithm. A long division method is introduced for finding the similarity transformation matrix.

Shieh, L. S.↗
DOE OSTI2013

Mathematical framework for the analysis of dynamic stochastic systems with the RAVEN code

RAVEN (Reactor Analysis and Virtual control Environment) is a software code under development at Idaho National Laboratory aimed at performing probabilistic risk assessment and uncertainty quantification using RELAP-7, for which it acts also as a simulation controller. In this paper we will present the equations characterizing a dynamic stochastic system and we will then discuss the behavior of each stochastic term and how it is accounted for in the RAVEN software design. Moreover we will present preliminary results of the implementation. (authors)

97 MATHEMATICS AND COMPUTING↗