feasibility, uncertainty and interpolation
DESCRIPTION
Feasibility, uncertainty and interpolation. J. A. Rossiter (Sheffield, UK). Overview. Predictive control (MPC) Interpolation instead of optimisation Invariant sets Combining invariant sets Illustrations Conclusions. BACKGROUND. Notation. Assume a state space model and constraints - PowerPoint PPT PresentationTRANSCRIPT
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Feasibility, uncertainty and interpolation
J. A. Rossiter (Sheffield, UK)
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IEEE Colloquium, April 4th 20052
Overview
Predictive control (MPC) Interpolation instead of optimisation Invariant sets Combining invariant sets Illustrations Conclusions.
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IEEE Colloquium, April 4th 20053
BACKGROUND
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IEEE Colloquium, April 4th 20054
Notation
Assume a state space model and constraints
Let the control law be Define the maximal admissible set (MAS), that
is region within which constraints are met, as
1 ;
; ;
k k k k k
k k
x Ax Bu y Cx
u u u x x x
k ku Kx
0 0 0{ : }S x M x d
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IEEE Colloquium, April 4th 20055
Invariant set and closed-loop trajectories
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5
0 5 10 15 20 25 30-2
-1
0
1
2Inputs
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IEEE Colloquium, April 4th 20056
Minimise a performance index of the form
Can write solutions as
1,..., 0
0
;
min . . ;k k nc
c
k
T Tk k k k k
u u k
k n
u u u
J x Qx u Ru s t x x x
x S
Predictive control
, 1, ,
,k k k c
k k c
u Kx c k n
u Kx k n
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IEEE Colloquium, April 4th 20057
Impact on invariant sets of adding d.o.f.
-3 -2 -1 0 1 2 3-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
OMPC (nc=10)
OMPC (nc=5)
OMPC (nc=2)
S1
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IEEE Colloquium, April 4th 20058
Observations
If terminal control is optimal, then the terminal region may be small. – Need large d.o.f. to get large feasible region.– Good performance
If terminal control is detuned, terminal region may be large.– Small d.o.f. to get large feasible region.– Suboptimal performance.
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IEEE Colloquium, April 4th 20059
INTERPOLATION
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IEEE Colloquium, April 4th 200510
Alternative strategy
Interpolation is known to:1. Allow efficient (often trivial) optimisations.2. Combine qualities of different strategies.
Interpolate between K1 and K2 where: K1 has optimal performance but possibly a
small feasible region K2 has large feasible region.
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IEEE Colloquium, April 4th 200511
MAS with K1 and K2
-4 -3 -2 -1 0 1 2 3 4-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
x1
x 2
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IEEE Colloquium, April 4th 200512
How to interpolate
A simple summary: split the state into 2 components and predict separately through the 2 closed-loop dynamics, then recombine.
Decomposition into x1 and x2 to ensure constraint satisfaction.
)()()(
)()1(
)()1( 2211
222
111
21
kxkxnkx
kxkx
kxkx
xxxnn
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IEEE Colloquium, April 4th 200513
Feasible regions with Interpolation
Ellipsoidal invariant sets Find max. volume feasible
invariant ellipsoid. By necessity conservative in
volume. Can be computed easily,
even with model uncertainty. Generalised interpolation
algorithm takes convex hull of several ellipsoids.
SDP solver required.
Polytopic invariant sets Can use MAS – maximum
possible feasible regions. Easily computed for nominal
case only. Various interpolation
algorithms for certain case. Still limited to convex hull of
underlying sets. Optimisation requires QP or
LP.
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IEEE Colloquium, April 4th 200514
Weakness of ellipsoidal sets
-4 -2 0 2 4 6 8-8
-6
-4
-2
0
2
4
x-plane
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IEEE Colloquium, April 4th 200515
Feasible regions (figures)
-3 -2 -1 0 1 2 3-1.5
-1
-0.5
0
0.5
1
1.5
GIMPCS
2
S1
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IEEE Colloquium, April 4th 200516
When to use Interpolation?
Which is more efficient: – A normal MPC algorithm with d.o.f.?– An interpolation?
ONEDOF interpolations have only one d.o.f. but severely restricted feasibility.
General interpolation requires nx d.o.f. (nx the state dimension).
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IEEE Colloquium, April 4th 200517
Feasible regions with general interpolation, ONEDOF and nc d.o.f.
-3 -2 -1 0 1 2 3-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
GIMPCS
2
S1
OMPC (nc=10)
OMPC (nc=5)
OMPC (nc=2)
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IEEE Colloquium, April 4th 200518
Weaknesses of interpolation
1. Algorithms using MAS can only be applied to the nominal case.
2. Easy to show that uncertainty can cause infeasibility and instability.
3. Need modifications to cater for uncertainty.
Here we consider changes to cater for LPV systems.
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IEEE Colloquium, April 4th 200519
POLYTOPIC INVARIANT SETS
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IEEE Colloquium, April 4th 200520
Polytopic invariant sets (MAS) for nominal systems
The computation of these is generally considered tractable.
Let constraints be
Then the MAS is given as
Where
for n large enough.[Redundant rows can be
removed in general.]
fCxk
dMxk
f
f
f
d
C
C
C
M
n
;
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IEEE Colloquium, April 4th 200521
Polytopic invariant sets for LPV systems
The computation of these is generally considered intractable.
Consider a closed-loop LPV system
Then computing all possible open-loop predictions.
Clearly, there is a combinatorial explosion in the number of terms.
),,(; 11 rkk Coxx
f
f
f
d
M
M
C
M
n
;1
;2
1
1
ri
i
i
i
M
M
M
M
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IEEE Colloquium, April 4th 200522
Polytopic invariant sets for LPV systems
There is a need for an alternative approach. [Pluymers et al, ACC 2005]
Specifically, remove redundant constraints from Mi before computing Mi+1.
This will slow the rate of growth and produce a tractable algorithm, if, the actual MAS is of reasonable complexity.
f
f
f
d
M
M
C
M
n
;
ˆ
ˆ1
;
ˆ
ˆ
ˆ
2
1
1
ri
i
i
i
M
M
M
M
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IEEE Colloquium, April 4th 200523
Robust and nominal invariant sets
-6 -4 -2 0 2 4 6 8-10
-5
0
5
Nominal
LPV
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IEEE Colloquium, April 4th 200524
Polytopic invariant sets and interpolation
MUST USE ROBUST SETS TO ENSURE FEASIBILITY!
We can simply use the ‘robust’ invariant sets in the algorithm developed for the nominal case.
Proofs of recursive feasibility and convergence carry across easily if the cost is replaced by a suitable upper bound.
(A quadratic stabilisability condition is required.)
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IEEE Colloquium, April 4th 200525
Summary
Polytopic invariant sets allow the use of interpolation with LPV systems and hence:
1. Large feasible regions.
2. Robustness.
3. Small computational load.
BUT:
General interpolation still only applicable to convex hull of underlying regions. This could be too restrictive.
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IEEE Colloquium, April 4th 200526
EXPLICIT OR IMPLICIT CONSTRAINT HANDLING
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IEEE Colloquium, April 4th 200527
Extending feasibility of interpolation methods
General interpolation does implicit not explicit constraint handling.
So:1. membership of the set implies the trajectories are
feasible.2. non-membership may not imply infeasibility.
Therefore, we know that feasibility may be extended beyond the convex hull in general, but how ?
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IEEE Colloquium, April 4th 200528
Implicit constraint handling
With ellipsoidal invariant sets this is obvious.
Constraints are converted into an LMI, with some conservatism because of:1. Asymmetry2. Conversion of linear
inequalities to quadratic inequalities.
A trivial example of this might be
or
432
KxKxKxu
u TT
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
x1
x 2
Quadratic Linear
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IEEE Colloquium, April 4th 200529
Conservatism with linear inequalities
Define the invariant sets associated to K1, K2,… to be
Then, general interpolation first splits x into several components and uses the constraints
,...:,: 2211 dxNxSdxNxS
ii
i
i
iii
xKu
Sx
xxx ;
1
0;...21
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IEEE Colloquium, April 4th 200530
Conservatism with linear inequalities (b)
The constraint enforces feasibility.
However, consider the following hypothetical illustration:
This implies that
iii Sx
xnkxdxN
xmkxdxN
22222
11111
)(
)(
xxxx
xxxx
)min()min(
)max()max(
21
21
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IEEE Colloquium, April 4th 200531
Remarks
The constraint
is necessary with ellipsoidal invariant sets as one can not check predictions explicitly against constraints.
This is not the case with polytopic invariant sets. Hence we propose to relax this condition and hence
increase feasible regions. Remove the two conditions
1;0; iiiii Sx
0; iiii Sx
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IEEE Colloquium, April 4th 200532
Relaxed constraints
General interpolation can be composed as
We propose to replace this as a single inequality:
NOTE: No longer any variables!
1
0
...
;21
222
111
i
i
xxx
dxN
dxN
...
;
21
2211
xxx
dxNxN
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IEEE Colloquium, April 4th 200533
Structure of inequalities (nominal case)
Consider the predictions
And hence the explicit constraints are
...)()()();(
)(
)2(
)1(
21
2
rkxrkxrkxkx
nkx
kx
kx
i
ni
i
i
i
i
i
f
f
f
kx
C
C
C
kx
C
C
C
nn
...)()( 2
2
22
2
1
1
21
1
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IEEE Colloquium, April 4th 200534
ILLUSTRATIONS
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IEEE Colloquium, April 4th 200535
Illustrations
1. There can be surprisingly large increases in feasibility.
2. Probably because the directionality of trajectories for each controller are different.
-4 -3 -2 -1 0 1 2 3 4-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
GIMPC2
GIMPCS
2
S1
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IEEE Colloquium, April 4th 200536
Extensions to the LPV case
Unfortunately, explicit constraint handling requires a direct link between the prediction equations and the inequalities.
However, the algorithm for finding polytopic invariant sets in the LPV case, relied, for efficiency, on removing redundant constraints from the predictions.
dxNxN
f
f
f
kx
C
C
C
kx
C
C
C
nn
22112
2
22
2
1
1
21
1
...)()(
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IEEE Colloquium, April 4th 200537
Extensions to the LPV case (b)
For the original GIMPC, sets S1, S2,.. could be described as efficiently as possible. There was no need for mutual consistency because constraint handling was implicit.
Notably, all redundant inequalities could be eliminated.
When doing explicit constraint handling, redundant constraints cannot be eliminated from Si, just in case the overall x(k+j) for that row is against a constraint!
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IEEE Colloquium, April 4th 200538
Constraints for general interpolation with LPV systems
Algorithms can be written to formulate the inequalities, but suffer more from the combinatorial growth problems outlined earlier.
Assuming the resulting sets are not too large, proofs of convergence and feasibility are straightforward.
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IEEE Colloquium, April 4th 200539
Illustration of inequalities
N1 N2 other Total d.o.f.
GIMPC 30 12 2 44 3
GIMPC2 412 412 0 412 2
RMPC
(nc=5)
448 5
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IEEE Colloquium, April 4th 200540
Conclusions
Interpolation is known to facilitate reductions in complexity at times, particular for low dimensional systems. However most work has focussed on the nominal case.
Some earlier interpolation algorithms used implicit constraint handling to cater for uncertainty. This could lead to considerable conservatism.
We have illustrated:– How interpolation can be modified to overcome this
conservatism and the associated issues (recently submitted).– how polytopic robust MAS might be computed and used in
MPC (to be published IFAC and ACC, 2005).– how to use polytopic robust MAS with interpolation (recently
submitted).