Chapter 2: State Space Definitions Flashcards

1
Q

Advantage of State Space Design (3)

A
  1. Reveals all internal poles/zeros
  2. Linear algebra can be used
  3. Supports design and analysis of feedback control systems
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2
Q

Matrices of State Space Equations (4)

A
  1. A - System Matrix
  2. B - Input Matrix
  3. C - Output Matrix
  4. D - Feedforward Matrix
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3
Q

State Space and Transfer Equation

  1. C.T.
  2. D.T.
A
  1. H(s) = C * inverse(sI-A) * B + D

2. H(z) = C * inverse(zI-A) * B + D

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4
Q

Transformation of State Space Matrices where x’ = Tx (4)

A
  1. A’ = T * A * inverse(T)
  2. B’ = inverse(T) * B
  3. C’ = C * inverse(T)
  4. D’ = D
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5
Q

Parallel Canonical Form Properties

A
  1. Poles of transfer function are eigenvalues of the system

2. H(s) = bn + c1/(s-p1) + … + cn/(s-pn)

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6
Q

Cascade Canonical Properties

A
  1. Diagonal elements are eigenvalues of system matrix and poles of transfer function
  2. Series of 1st Order D.E. H(s) = H1(s) * H2(s) * … * Hn(s)
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7
Q

Phase Variable Canonical Properties

A
  1. Used when there are no input derivatives.
  2. x1 = y, x2 = x1’, … , xn = xn-1’
  3. Simplifies control gain selection
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8
Q

Controllable Canonical Properties

A
  1. Used to design optimal pole placement
  2. Y(s)/U(s) = P(s)/U(s) * Y(s)/P(s)
  3. Output of last integrator is x1 = p, xn is output to first integrator
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9
Q

Observable Canonical Properties

A
  1. Estimation of state vector by observing input/output of system
  2. Observability canonical form is used if observable form doesn’t exist
  3. Obtain highest order on left side and integrate
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10
Q

Static Equilibrium

  1. C.T. State
  2. D.T. State
  3. Output
A
  1. x’ = f(xe, u0) = 0
  2. x(k+1) = f(xe,u0) = xe
  3. ye = g(xe,u0)
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11
Q

Linearization of Function

A
  1. Taylor Series Approximation

2. f(xe + Dx) = f(xe) + df/dx Dx + 1/2! d2f/dx2 Dx^2 + … where x = xe

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12
Q

Small Signal Model

f(xe + D*x)

A

f(xe) + df/dx D*x where x = xe

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