robust and optimal control, spring 2015 - fujita laboratory · 2020. 4. 28. · instructor: prof....
TRANSCRIPT
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C.1 Perturbations of Coprime FactorsC.2 Robust Stabilization
C. Loop Shaping Design
C.3 Loop Shaping Design ProcedureC.4 Design Example
Reference:[SP05] S. Skogestad and I. Postlethwaite,
Multivariable Feedback Control; Analysis and Design,Second Edition, Wiley, 2005.
[SP05, Sec. 4.1.5]
[SP05, Sec. 9.4.1]
[SP05, Sec. 9.4.2]
Robust and Optimal Control, Spring 2015Instructor: Prof. Masayuki Fujita (S5-303B)
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Robust Stabilization
2
RemarkPole/Zero CancellationLoop Shaping (NS, NP+RS)
−
Mixed Sensitivity Problem
K. Glover and D. McFarlane, “Robust stabilization of normalized coprime factor plant descriptions with bounded uncertainty,” IEEE TAC, 34-8, 821-830, 1989
K. GloverD. McFarlane
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Perturbations to Coprime Factors
[Ex.]
Coprime Factorization of Transfer Functions
Controllable Observable
Transfer Function Matrix
There are no “common zeros” in and in the right half plane
(Left) (Right)
〜1 3
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Normalized Coprime Factorization
[Ex.]
Note: Given any coprime factorization of thenfor (need the poles and zeros of to be in the LHP)
(NLCF: Normalized Left-Coprime Factorization)
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Coprime Factor Uncertainty [SP05, pp. 304, 365]
[SP05, p. 122]Nominal Plant Model
The set of Plant Models
(Left Coprime Factorization)
Full-matrix
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6Coprime factor perturbations are not unique.Note:
Coprime Factor Uncertainty[SP05, p. 122]Nominal Plant Model
The set of Plant Models
(Left Coprime Factorization)
[Ex.]
with
If is real constant, then pole is moved to . Hence poles
move across with small but very large changes.
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[AM09, pp. 349-352]Vinnicombe Metric ( -gap Metric) [ZD97, Chap.17]
[ZD97] K. Zhou with J.C. Doyle, Essentials of Robust Control, Prentice Hall, 1997.
[AP09, Ex 12.2][AP09, Ex 12.3]
A distance measure that is appropriate for closed loop systems
Vinnicombe metric ( -gap Metric)G. Vinnicombeif
〜4 6
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Robust Stability Condition
Robust Stability Condition
Closed loop system (LFT)
[SP05, pp. 305, 366]
Plant Model
The set of Plant Models
( Small Gain Theorem)
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Control Synthesis
All Stabilizing Controllers (Youla Parameterization)
Nominal Plant Model
Double Bezout Equation
for
Closed-loop Transfer Function
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NLCF Robust Control ProblemSub-optimal Control Problem
Given , find all stabilizing controllers such that
SolutionMinimum Value of -norm
Maximum eigenvalues of the matrixSub-optimal Solution ( controller)
(Maximum Stability Margin)
: a transfer function satisfying
Central controller ( )
[SP05, p. 366]〜7 12
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D. McFarlane and K. Glover, “A loop Shaping Design Procedure Using Synthesis,” IEEE TAC, 37-6, 759-769, 1992
Loop Shaping Design Procedure (LSDP)
The shaped plant and controller
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Loop Shaping Design
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Loop Shaping Design Procedure (LSDP)STEP 1: Loop Shaping
To shape the plant using shaping functions and, are chosen to satisfy keeping unstable pole of the model
STEP 2: Robust Stabilization
Find such that
STEP 3: Controller
[SP05, p. 368]
(maximize ) Target Loop
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LSDP: Decision Method of Weights ,
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LSDP in MIMO SystemsFor Low Frequencies
For High Frequencies
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Generalized Weighted Formulation
: minimum sensitivity problem: robust control problem for additive uncertainty: mixed sensitivity problem
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Robust Performance
−
RP: Robust Performance
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No -iteration is required in the solution .
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Advantage of LSDP
In case a process has a pole on the imaginary axis, LSDP does not require the additional operation to solve the problem.
LSDP can permit a wider error of the model
-iterationhinfsyn/dksyn :(3)
(4)
hinfsyn : Assumptions
(A2) is controllable and is observable(A1) is stabilizable and is detectable
Full rank on the imaginary axis
(6)
[SP05, p. 372]
LSDP is relatively easy to use, based on classical loop-shaping ideas(1)There exists a closed formula for the optimal cost , which in turn corresponds to a maximum stability margin
(2)
Except for special systems, ones with all-pass factors, there are no pole-zero cancellations between the plant and controller
(5)
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Output argumentKCl
LTI ControllerLTI Closed-loop system
MATLAB Command
GGd
Input argument(Generalized) LTI PlantDesired Loop-shape (LTI model)
loopsyn
[K, Cl, Gam, info] = loopsyn( G, Gd, RANGE )
(option) RANGE Desired frequency range for loop-shaping(Default)
Gam Loop-shaping accuracy . : perfect fit.Info Information of output result
(option) Info.W,Info.Gs, Info.Ks, Info.range
satisfying
optimal controller synthesis for LTI plant
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Output argumentKCl
LTI ControllerLTI Closed-loop system
MATLAB Command
GW1, W2
Input argument(Generalized) LTI PlantWeights
ncfsyn
[K, Cl, Gam, info] = ncfsyn( G, W1, W2, ‘ref’ )
(option) ‘ref’ Compute normalized coprime factor loop-shaping controller
Gam Loop-shaping accuracy . : perfect fit.Info Information of output result
Loop shaping design using Glover-McFarlane method
〜15 17
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The 2-by-2 NASA HIMAT aircraft model
: elevon actuator: canard actuator
: attitude angle: angle of attack
(Loop Shaping of HIMAT Pitch Axis Controller)
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HIMAT: Nominal Plant Model
% NASA HiMAT model G(s) ag =[ -2.2567e-02 -3.6617e+01 -1.8897e+01 -3.2090e+01 3.2509e+00 -7.6257e-01;
9.2572e-05 -1.8997e+00 9.8312e-01 -7.2562e-04 -1.7080e-01 -4.9652e-03; 1.2338e-02 1.1720e+01 -2.6316e+00 8.7582e-04 -3.1604e+01 2.2396e+01;
0 0 1.0000e+00 0 0 0; 0 0 0 0 -3.0000e+01 0; 0 0 0 0 0 -3.0000e+01];
bg = [ 0 0; 0 0; 0 0; 0 0; 30 0; 0 30]; cg = [ 0 1 0 0 0 0; 0 0 0 1 0 0]; dg = [ 0 0; 0 0]; G=ss(ag,bg,cg,dg);
MATLAB Command
State Space Form (Matrix Representation)
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-5.6757, -0.2578, -30, -30,0.6898 0.2488i
-0.0210Unstable
HIMAT: Nominal Plant Model〇〇
ControllabilityObservabilityPoles (Stability)
Zeros
Frequency ResponseStep Response for Nominal Plant Model
0.5
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Approximately a bandwidth of 10 rad/sDesired Loop Shape
Target loop
HIMAT: Specifications and Open-loop
Gd= tf( 10, [1 .001] ) ;[K,CL,GAM,INFO]=loopsyn(G,Gd); sigma(Gd,‘b',G*K,'r',Gd/GAM,‘g:',Gd*GAM,‘g:',{.1,100})
MATLAB Command
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HIMAT: Controller
sigma(K);MATLAB Command
Numerical problems or inaccuracies may be caused too high orderDifficult to implement
Order: 16
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HIMAT: Controller Model Reduction
size(K)
hsv = hankelsv(K); semilogy(hsv,'*--'), grid title('Hankel singular values of K'), xlabel('Order')
Kr = reduce(K,9); order(Kr)
sigma(K,'b',K-Kr,'r-.') legend('K','error K-Kr')
MATLAB Command
Order: 16 → 9
Step responses
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HIMAT: Sensitivity
T = feedback(G*K,eye(2)); S = eye(2)-T;sigma(inv(S),'m',T,'g',L,'r--',Gd,'b',Gd/GAM,'b:',Gd*GAM,'b:',{.1,100})
MATLAB Command
-3
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HIMAT: Designed Weight
sigma(INFO.Gs,‘b',G,'r',INFO.W,‘g',{.01,100});MATLAB Command
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0.5
HIMAT: Step Response (closed loop)
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: Integer[Ex.]
Bezout Identity: Proper Stable Transfer Functions
: Coprime
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Youla Parameterization
Coprime Factorization
: Proper Stable Transfer Functions
[SP05, Ex. 4.1]
: Integer
Unstable PlantCase 2:
Coprime: No common right-half plane(RHP) zeros
(*)
[SP05, p. 149]
[SP05, p. 122]
[SP05, Ex.] : (*)
1
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Youla ParameterizationUnstable PlantsCase 2:
A Stabilizing Controller
[SP05, Ex.]
Gang of Four
Affine Functions of
All Stabilizing Controllers
[SP05, p. 149]2
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Coprime Factorization: State-space ProcedureObserver design
State feedback pole assignment
3
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[AM09, Ex 12.2]
(a) Step response (open loop) (b) Step response (closed loop)
open loop
When Are Two Systems Similar ?4
[AM09] K.J. Astrom and R.M. Murray, Feedback Systems, Princeton Univ. Press, 2009.Similar in Open Loop but Large Differences in Closed Loop
closed loop
,
[AM09, pp. 349-352]
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34Different in Open Loop but Similar in Closed Loop
(a) Step response (open loop) (b) Step response (closed loop)
open loop
closed loop
5
[AM09, Ex 12.3]
When Are Two Systems Similar ? [AM09, pp. 349-352]
,
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Vinnicombe Metric ( -gap Metric)
[Zhou98] K. Zhou with J.C. Doyle, Essentials of Robust Control, Prentice Hall, 1998.
[Zhou98, Chap.17]
: the smallest value ofthat perturbs into is called the gap between and
Ifwith and will also be stable
,
: the -gap between and
Note: Both and are metrics (i.e. distance measures)(1) (2)(3)(4) (Triangle inequality)
Ifthen we have closed loop stability of and
,
gives the radius (in terms of the distance in the -gap metric)of the largest “ball” of plants stabilized by
then the closed loop system
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Examples of NLCF Robust Control Problems
A Practical Implementation
Two-degrees of freedomloop-shaping controller
Discrete-time controllers, Anti-windup, Bumpless transferImplementation Issues [SP05, p. 380]
Observer-based Structure forloop-shaping controller
Output from shaped plant
Command
Input to shaped plant
[SP05, pp. 370-381] 7
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, ,,
,
,
Riccati equalitiessatisfying that
,where
where
NLCF Robust Control Problem
Nominal Plant Model
Sub-optimal Solution ( controller)
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Observer-based StructureCentral controller ( )
,where
FeaturesObserver gain is automatically designedObserver gain is related to both and (Riccati Solutions)
Cost Function
9
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Computation
Theorem
is stable
has no eigenvalues on imaginary axis
Graphical Test
-norm-norm
Controllability/ObservabilityHamiltonian Matrix
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Frobenius synthesis with Hadamard weightGiven , find all stabilizing controllers such that
F. van Diggelen and K. Glover, “A Hadamard weighted loop shaping design procedure,” Proc. 31st IEEE CDC, 2193-2198, 1992
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Stability Margin
The closed loop will be stable for all
(for good robustness)
In SISO Systems
Proof:
for
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LSDP in SISO SystemsFor Low frequencies
,
For High frequencies
,
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Robust Performance in the -Gap Metric
If stabilizes with then will also stabilizeA bound on the robust performance
(The derivation of this is due to Vinnicombe and is non-trivial)
This inequality is a slightly stronger inequality than
which is also true and shows clearly how the performance can be degraded by perturbations to the plant and controller
Perturbed performance
Nominal performance
Plant perturbation
Controller perturbation
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MATLAB command: “magshape”
Design Method1.
(graphical user interface)
Express the design specifications in terms of loop shapes and shaping filters.
2. Specify the shaping filters by their magnitude profile. This is done interactively with the graphical user interface magshape.
3. Specify the control loop structure with the functions sconnect and smult, or alternatively with Simulink.
4. Solve the resulting problem with one of the synthesis functions.
To impose a given roll-off rate in the open-loop response, it is often desirable to use nonproper shaping filters. Meanwhile, “magshape” approximates them by high-pass filters. A drawback of this approximation is the introduction of fast parasitic modes in the filter and augmented plant realizations, which in turn may cause numerical difficulties.
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STEP 1 Input filter name STEP 2 Put desired points
STEP 3 shape a Filter
Output the filter automatically(SYSTEM matrix form)
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MATLAB command: “sderiv”Alternatively, you can use “sderiv” to include nonproper shaping filters in the loop-shaping criterion. This function appends a SISO PD component to selected Input/Output of given LTI system.Design Method
1.
2.
Specify the proper “low-frequency” part of the filters with magshape.Augment the plant with these low-pass filters.
3. Add the derivative action of the nonproper filters by applying sderiv to the augmented plant.
To specify more complex nonproper filters,
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