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state space control - 21.24

21.7LINEAR QUADRATIC GAUSSIAN (LQG) COMPENSATORS

•Uses an optimal regulator and estimator.

•Stability is guaranteed if the model is accurate. Verification is needed

•The design focuses on,

A cost function for the system state, the design tries to minimize this The relative weighting for the process and sensor noise

•

21.8VERIFYING CONTROL SYSTEM STABILITY

•Introduce random errors to the system model and test for stability

•Use the Nyquist Stability Theorem,

state space control - 21.25

Z = N + P where,

P =

poles of G(s)Gc(s) in the right hand plane

Z =

closed loop poles in the right hand plane

N = number of clockwise loops in the Nyquist Diagram about -1

Z = 0, N =

–P

For stability

Note: Larger loops indicate a more stable system. Tight loops are very sensitive to parameter variations.

21.8.1Stability

•The stability function is defined using the Nyquist plot.

state space control - 21.26

Define the points for the Nyquist plot

L( s) = G( s) Gc( s)

LN( s) = GN( s) Gc ( s)

LA( s) = GA( s) Gc( s)

The distance, ’d’, from the point at -1 to the calculated point is found as a function.

d( jω ) = LN( jω ) – ( – 1 + 0j)

The sensistivity ’S’ is defined as the inverse of the distance from the point at -1.

S( jω )

 

1

 

1

 

=

-------------

=

--------------------------

d( jω )

1 + LN

( jω )

21.8.2Bounded Gain

•In practical terms system gain is limited by real components. As a result it is important to ensure that the design will not exceed the maximum values available.

state space control - 21.27

The boundedness of the system can be tested by ensuring that the sensitivity always remains less than some given value ’gamma’ for all frequencies. Note: this requires that D must be smaller than gamma.

S( jω ) < γ

C( jω I – A) –1B + D < γ

The Hamiltonian matrix is defined below. It does not have purely complex Eigenvalues.

H =

 

A + B( γ2I – DTD) –1DTC

B( γ2I – DTD) –1BT

 

–CT( I + D( γ2I – DTD) –1DT) C – AT – CTD( γ2I – DTD) –1BT

 

 

 

 

 

 

Or, if D=0,

 

 

 

 

 

 

 

A

BBT

H =

 

---------

 

γ2

 

–CTC –AT

 

 

 

 

 

This matrix can then be tested, if there are no purely complex eigenvalues, the system is bounded.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

BBT

 

 

 

 

 

 

BBT

 

 

 

 

 

 

 

 

 

 

 

 

 

λI – H

 

=

 

λ 0

–

 

A

---------

 

 

=

 

 

λ – A –---------

 

 

 

 

 

2

 

 

 

 

 

2

 

 

 

 

 

 

0 λ

 

 

 

 

γ

 

 

 

 

 

 

γ

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

–CTC –AT

 

 

 

 

 

 

CTC λ + AT

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2

 

 

 

 

 

 

 

T

 

 

BB

T

C

T

= λ

 

 

 

 

 

 

 

+

–

 

C

 

 

( 1) +λ ( – A + A )

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

---------------------

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

γ2

 

 

 

etc....

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