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Space, state

For heavy molecules with very small rotational state spacing, this limit on AJ puts severe upper limits on the amount of energy that can be taken up in the rotations of a heavy molecule during a collision. Despite these limitations, P(E, E ) distributions have been obtained by inverting data of the type described here for values of AE in the range -1500 cm > AE > -8000 cnD for the two donor molecules pyrazine and hexafluorobenzene with carbon dioxide as a bath acceptor molecule [15,16]. Figure C3.3.11 shows these experimentally derived... [Pg.3011]

The reader may think of a finite dimensional subspace of the original state space. This subspace may, e.g., be associated with a suitable discretization in space. For a generalization of Thm. 3 to the infinitely dimensioned case, see [5]. [Pg.386]

A key featui-e of MPC is that a dynamic model of the pi ocess is used to pi-edict futui e values of the contmlled outputs. Thei-e is considei--able flexibihty concei-ning the choice of the dynamic model. Fof example, a physical model based on fifst principles (e.g., mass and energy balances) or an empirical model coiild be selected. Also, the empirical model could be a linear model (e.g., transfer function, step response model, or state space model) or a nonhnear model (e.g., neural net model). However, most industrial applications of MPC have relied on linear empirical models, which may include simple nonlinear transformations of process variables. [Pg.740]

State-space methods for control system design... [Pg.232]

The state-space approach is a generalized time-domain method for modelling, analysing and designing a wide range of control systems and is particularly well suited to digital computational techniques. The approach can deal with... [Pg.232]

The state variables are the smallest number of states that are required to describe the dynamic nature of the system, and it is not a necessary constraint that they are measurable. The manner in which the state variables change as a function of time may be thought of as a trajectory in n dimensional space, called the state-space. Two-dimensional state-space is sometimes referred to as the phase-plane when one state is the derivative of the other. [Pg.232]

The block diagram of the system is shown in Figure 9.10. Continuous state-space model From equations (9.77)-(9.81)... [Pg.290]

If Pmfv) and the plant uncertainty A(.v) are combined to give P(.v), then Figure 9.29 can be simplified as shown in Figure 9.30, also referred to as the two-port state-space representation. [Pg.314]

Fig. 9.30 Two-port state-space augmented plant and controller. Hence the augmented plant matrix P(.v) in Figure 9.30 is... Fig. 9.30 Two-port state-space augmented plant and controller. Hence the augmented plant matrix P(.v) in Figure 9.30 is...
This tutorial looks at how MATLAB commands are used to convert transfer functions into state-space vector matrix representation, and back again. The discrete-time response of a multivariable system is undertaken. Also the controllability and observability of multivariable systems is considered, together with pole placement design techniques for both controllers and observers. The problems in Chapter 8 are used as design examples. [Pg.401]

This converts a transfer function into its state-space representation using tf2ss(num, den) and back again using ss2tf (A,B, C, D, iu) when iu is the itii input u, normaiiy i. [Pg.402]

Example 8.4 transfer function to state space representation... [Pg.402]

The conversion from state-space to transfer function has produced some smaii erroneous numerator terms, which can be negiected. These errors reiate to the condition of A, and wiii increase as the condition number increases. [Pg.402]

The command mksys packs the plant state-space matrices ag, bg, eg and dg into a tree structure ss g. [Pg.416]

Coupled-map Lattices. Another obvious generalization is to lift the restriction that sites can take on only one of a few discrete values. Coupled-map lattices are CA models in which continuity is restored to the state space. That is to say, the cell values are no longer constrained to take on only the values 0 and 1 as in the examples discussed above, but can now take on arbitrary real values. First introduced by Kaneko [kaneko83]-[kaneko93], such systems are simpler than partial differential equations but more complex than generic CA. Coupled-map lattices are discussed in chapter 8. [Pg.17]


See other pages where Space, state is mentioned: [Pg.390]    [Pg.298]    [Pg.77]    [Pg.1126]    [Pg.232]    [Pg.320]    [Pg.405]    [Pg.407]    [Pg.4]    [Pg.294]    [Pg.3]   
See also in sourсe #XX -- [ Pg.232 ]

See also in sourсe #XX -- [ Pg.14 , Pg.16 , Pg.19 , Pg.29 , Pg.159 ]

See also in sourсe #XX -- [ Pg.82 ]




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And state-space concept

Angular momentum, phase-space transition state

Angular momentum, phase-space transition state geometry

Angular momentum, phase-space transition state potential

Canonical variates state-space models

Chaotic transitions phase-space transition states

Complete active space state interaction

Computer-generated state space form

Continuous state-space

Continuous state-space processes

Dimensionality phase-space transition states

Discrete state-space

Discrete state-space processes

Discrete time state space model

Discrete time state space model description

Dynamics in state space

Ecosystems state space

Effect-concentration state space for the indirect link model

Effect-plasma drug concentration state space for tolerance

Examples of State Space Model Identification

Exponential unitary transformations of states in Fock space

Free-electron states for crystals with non-symmorphic space groups

Hamiltonian systems phase-space transition states

Hilbert space coherent states

Hilbert space truncated states

Hyperbolicity phase-space transition states

Implicit state space form

Implicit state space solution

Invariant structures phase-space transition states

Kinetic energy phase-space transition states

Linear process model state-space representation

Linear state-space framework

Linear system state space form

Linearization, phase-space transition state

Liquid state empty space

Mapping biochemical state space

Markov Chains with Continuous State Space

Markov chain continuous state space

Maximum-Likelihood State-Space Estimates

Models subspace state-space

Molecular Modeling - Mapping Biochemical State Space

Nonlinear dynamics phase-space transition states

Normally hyperbolic invariant manifolds phase-space transition states

Numerical Reduction to State Space Form

Partial Exploration of State Spaces and Hypothesis Test for Unsuccessful Search

Particle state space

Particle state space defined

Particle state space number density

Perturbation theory phase-space transition states

Phase space theory orbiting transition state

Phase-space transition states

Phase-space transition states Hamiltonian dynamics

Phase-space transition states Melnikov integral

Phase-space transition states additional potentials

Phase-space transition states atomic clusters

Phase-space transition states breakdown

Phase-space transition states dimensions

Phase-space transition states examples

Phase-space transition states general equations

Phase-space transition states momentum

Phase-space transition states nonlinearities

Phase-space transition states reaction paths

Phase-space transition states relative equilibrium

Phase-space transition states stationary points

Phase-space transition states structure

Phase-space transition states temperature

Pump/probe state space

Real-space distribution, electronic states

STATE-SPACE METHODS FOR CONTROL SYSTEM DESIGN

Saddle regions phase-space transition states

Solid state empty space

Space Applications (United States)

Space charge limited currents localized states

Space of Structure States

Space of states

Spin-space states

Stable/unstable manifolds phase-space transition states

State Space Form and the Drazin ODE

State Space Form of Linear Constrained Systems

State Space Model Identification

State Space of Ecosystems

State Vectors in Hilbert Space

State space analysis

State space curve

State space defined

State space dynamics

State space for different initial conditions

State space form

State space modeling

State space modeling example

State space optimization

State space representation

State space resonance energy operator

State space reversible adiabat

State space system matrix

State space transfer rate operator

State space transformation

State-Space Model for Control Design

State-Space Model for Time Series

State-Space Modelling of Time Series

State-selective active-space methods

State-space approach

State-space concept

State-space equations

State-space feedback linearization

State-space formulation

State-space methods

State-space model

State-space models disturbance

State-space models linear

State-space models nonlinear

Steady-State Concentration Profile in Spherical Space

Stochastic reaction kinetics nonequilibrium thermodynamics of state-space

Sufficient Theories for State-Space Formulation

Sufficient theory state-space formulation

Tangency, phase-space transition states

Temperature dependence phase-space transition states

The state space

The state-space-approach

Thermodynamic state space

Time scales phase-space transition states

Time series state-space approach

Time-Invariant Markov Chains with Finite State Space

Time-dependent equations phase-space transition states

United States National Aeronautics and Space

United States National Aeronautics and Space Administration

United States space program

Vibrational frequency phase-space transition states

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