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Related Concept Videos

State Space Representation01:27

State Space Representation

171
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Transfer Function to State Space01:23

Transfer Function to State Space

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
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Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

173
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
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Signal and System01:26

Signal and System

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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
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Structure of Multi-State Correlation in Electronic Systems.

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Researchers developed a matrix density functional theory for excited states. A new theorem shows that a single state

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Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Materials Science

Background:

  • The Hohenberg-Kohn density functional theory (DFT) accurately describes ground-state electronic properties.
  • Extending DFT to excited states remains a significant challenge in quantum chemistry.
  • Matrix density functionals offer a potential framework for describing multiple electronic states.

Purpose of the Study:

  • To establish rigorous conditions for Hamiltonian matrix functionals.
  • To introduce and characterize the correlation matrix functional for excited states.
  • To develop a theoretical foundation for efficient excited-state simulations.

Main Methods:

  • Formulating the Hamiltonian matrix as a density functional.
  • Representing the matrix density using auxiliary multiconfigurational wave functions.
  • Enforcing subspace invariance properties on the Hamiltonian matrix functional.

Main Results:

  • Derived rigorous conditions for the Hamiltonian matrix functional based on subspace invariance.
  • Established a fundamental theorem for the correlation matrix functional.
  • Demonstrated that a single state's correlation functional uniquely determines the entire subspace's correlation matrix functional.

Conclusions:

  • The study reveals the complex structure of electronic correlation within Hilbert subspaces.
  • The findings provide a novel theoretical framework for understanding and simulating excited states.
  • This work suggests a promising pathway for more efficient computational chemistry methods.