Linear systems design using state variable feedbacks
State variable feedback method for calculating linear automatic control systems
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State variable feedback method for calculating linear automatic control systems
The state variable constitutive equation of Bodner and Partom was used to calculate the load-strain response of Inconel 718 at 649 C in the root of a notch. The constitutive equation was used with the Bodner-Partom evolution equation and with a second evolution equation that was derived from a potential function of the stress and state variable. Data used in determining constants for the constitutive models was from one-dimensional smooth bar tests. The response was calculated for a plane stress condition at the root of the notch with a finite element code using constant strain triangular elements. Results from both evolution equations compared favorably with the observed experimental response. The accuracy and efficiency of the finite element calculations also compared favorably to existing methods.
The general theory of internal state variables are reviewed to apply it to inelastic metals in use in high temperature environments. In this process, certain constraints and clarifications will be made regarding internal state variables. It is shown that the Helmholtz free energy can be utilized to construct constitutive equations which are appropriate for metallic superalloys. Internal state variables are shown to represent locally averaged measures of dislocation arrangement, dislocation density, and intergranular fracture. The internal state variable model is demonstrated to be a suitable framework for comparison of several currently proposed models for metals and can therefore be used to exhibit history dependence, nonlinearity, and rate as well as temperature sensitivity.
State variable determination for linear time invariant plants with known parameters, noting noise suppressing characteristics of technique
Intentional nonlinearity in state variable feedback systems for gain insensitive designs
State variable techniques applied to optimal continuous linear feature extraction for binary Gaussian pattern recognition or detection problem
Regulator logic synthesis using state variable feedback for stationary linear plants
In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.
The established necessary conditions for optimality in nonlinear control problems that involve state-variable inequality constraints are applied to a class of singularly perturbed systems. The distinguishing feature of this class of two-time-scale systems is a transformation of the state-variable inequality constraint, present in the full order problem, to a constraint involving states and controls in the reduced problem. It is shown that, when a state constraint is active in the reduced problem, the boundary layer problem can be of finite time in the stretched time variable. Thus, the usual requirement for asymptotic stability of the boundary layer system is not applicable, and cannot be used to construct approximate boundary layer solutions. Several alternative solution methods are explored and illustrated with simple examples.
Control system response improvement by state variable feedback illustrated by frequency and transient response tests data from analog model
State variable approach for analysis of linear multivariable system with multiple eigenvalues
Integral and peak sensitivities defined for state- variable feedback control system
State variable feedback design of m-input, m- output time invariant linear systems requiring noninteraction and exact transfer functions, considering coupled core nuclear reactor
The modeling and simulation of large dc spacecraft power systems necessitates considering the spacecraft power system as an interconnection of modular components. This paper presents a state-variable-based approach for dc spacecraft power system modeling and simulation. Each modular component is treated as a two-port network, and a state model is written with the port voltages as the inputs. The state model of a component is solved independently of the other components using its state transition matrix. The state variables of each component are updated assuming that the inputs are constant. Network analysis principles are then utilized to calculate the component inputs.
Literature review of discontinuous state variables in calculus of variations
Computational technique for optimal control problems with state variable constraint
Dynamic programming computational approach to optimization with state variable discontinuities, treating problems as multistage optimization with continuous subarc stages
Two state-variable representations derived for continuous-time plant driven by control algorithm including zero-order hold and measurements sampled at mutliple rates by multiple-input/multiple-output moving-average processes. New representations enhance observability and controllability of plant. Applications include mathematical modeling of navigation systems including star trackers, gyroscopes, and accelerometers.