Estimates of the Statistics of Randomly Varying Parameters of Linear Systems
Mathematical model used in estimating statistics of randomly varying parameters of linear systems
IDENTIFICATION↗
SEARCH · Engineering Papers
Results for “linear 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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Effect of spacewise variations in a random load field on the response of a linear system
Effect of spacewise variations in random load field on response of linear system - statistical mechanics
LINEAR SYSTEM↗
ADAPTIVE STATE VECTOR CONTROL. SECTION 6- THE THEORY OF AVERAGE POWER OPTIMAL CONTROL OF LINEAR SYSTEMS
Adaptive state vector control - section 6, the theory of average power optimal control of linear systems
LINEAR SYSTEM↗
Asynchronous quadratic control for constrained hidden markov jump linear systems with incomplete MTPM and MOCPM
Abstract This paper investigates the quadratic optimal control problem for constrained Markov jump linear systems with incomplete mode transition probability matrix (MTPM). Considering original system mode is not accessible, observed mode is utilized for asynchronous controller design where mode observation conditional probability matrix (MOCPM), which characterizes the emission between original modes and observed modes is assumed to be partially known. An LMI optimization problem is formulated for such constrained hidden Markov jump linear systems with incomplete MTPM and MOCPM. Based on this, a feasible state-feedback controller can be designed with the application of free-connection weighting matrix method. The desired controller, dependent on observed mode, is an asynchronous one which can minimize the upper bound of quadratic cost and satisfy restrictions on system states and control variables. Furthermore, clustering observation where observed modes recast into several clusters, is explored for simplifying the computational complexity. Numerical examples are provided to illustrate the validity.
Zhu, Jin↗
On identifying transfer functions and state equations for linear systems.
Two methods are established for identifying constant-coefficient, C to the 2n power type of noise-free linear systems if the time response data of the input-output or of all states are known. 2n response data are required to identify an nth-order transfer function or state equation for an unknown linear system. The order of the unknown system can be identified by checking a sequence of determinants. The Z transform and its inversion are mainly used.
Shieh, L. S.↗
Analytic linear systems of differential equations in implicit form
Local theory developed for analytic linear systems of differential equations in implicit form
INTEGRAL EQUATION↗
Reconstruction of sampled input of a linear system from its sampled output
Computer processing which reconstructs sampled input of linear system from its sampled output and its unit step response
SAMPLED DATA↗
On the design of optimal linear systems using only output-variable feedback.
Optimal control of linear systems with state variables generated by linear feedback from output variables, considering time variance
Athans, M.↗
Fixed order dynamic compensation for multivariable linear systems
This paper considers the design of fixed order dynamic compensators for multivariable time invariant linear systems, minimizing a linear quadratic performance cost functional. Attention is given to robustness issues in terms of multivariable frequency domain specifications. An output feedback formulation is adopted by suitably augmenting the system description to include the compensator states. Either a controller or observer canonical form is imposed on the compensator description to reduce the number of free parameters to its minimal number. The internal structure of the compensator is prespecified by assigning a set of ascending feedback invariant indices, thus forming a Brunovsky structure for the nominal compensator.
Kramer, F. S.↗
Perfect decoupling of linear systems with discrete parameter uncertainties
A design procedure based on Gilbert's decoupling parameters for determining a fixed state feedback control law which decouples a linear system with discrete parameter uncertainties is described. Perfect decoupling conditions are established which involve a test for the existence of a solution to a system of linear equations. An actual solution of the linear equations yields the decoupling control law.
Dorato, P.↗
Iterative solution of large, sparse linear systems on a static data flow architecture - Performance studies
The applicability of static data flow architectures to the iterative solution of sparse linear systems of equations is investigated. An analytic performance model of a static data flow computation is developed. This model includes both spatial parallelism, concurrent execution in multiple PE's, and pipelining, the streaming of data from array memories through the PE's. The performance model is used to analyze a row partitioned iterative algorithm for solving sparse linear systems of algebraic equations. Based on this analysis, design parameters for the static data flow architecture as a function of matrix sparsity and dimension are proposed.
Reed, D. A.↗
Optimal output-feedback controllers for linear systems
Matrix method for optimal feedback control of linear systems
Levine, W. S.↗
Invariance in linear systems
Invariance conditions and state transition matrix of linear systems
Aggarwal, J. K.↗
Polynomial matrices, representations, and linear system theory
Frequency domain analogies, polynomial matrices, and linear systems
Wolovich, W. A.↗
On a stochastic control method for weakly coupled linear systems
The stochastic control of two weakly coupled linear systems with different controllers is considered. Each controller only makes measurements about his own system; no information about the other system is assumed to be available. Based on the noisy measurements, the controllers are to generate independently suitable control policies which minimize a quadratic cost functional. To account for the effects of weak coupling directly, an approximate model, which involves replacing the influence of one system on the other by a white noise process is proposed. Simple suboptimal control problem for calculating the covariances of these noises is solved using the matrix minimum principle. The overall system performance based on this scheme is analyzed as a function of the degree of intersystem coupling.
Kwong, R. H.↗
Linear system identification - The application of Lion's identification scheme to a third order system with noisy input-output measurements
A linear system identification technique developed by Lion is adapted for use on a third-order system with six unknown parameters and noisy input-output measurements. A digital computer is employed so that rapid identification takes place with only two state variable filters. Bias in the parameter estimates is partially eliminated by a signal-to-noise ratio testing procedure.
Brown, C. M., Jr.↗
Recursive inversion of externally defined linear systems
The approximate inversion of an internally unknown linear system, given by its impulse response sequence, by an inverse system having a finite impulse response, is considered. The recursive least squares procedure is shown to have an exact initialization, based on the triangular Toeplitz structure of the matrix involved. The proposed approach also suggests solutions to the problems of system identification and compensation.
Bach, Ralph E., Jr.↗
A program for identification of linear systems
A program has been written for the identification of parameters in certain linear systems. These systems appear in biomedical problems, particularly in compartmental models of pharmacokinetics. The method presented here assumes that some of the state variables are regularly modified by jump conditions. This simulates administration of drugs following some prescribed drug regime. Parameters are identified by a least-square fit of the linear differential system to a set of experimental observations. The method is especially suited when the interval of observation of the system is very long.
Buell, J.↗