SOS-Convex Lyapunov Functions for Switched...
Transcript of SOS-Convex Lyapunov Functions for Switched...
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SOS-Convex Lyapunov Functions for Switched Systems
Amir Ali Ahmadi
Goldstine Fellow, Mathematical Programming GroupIBM Watson Research Center
Joint work with:
Raphaël JungersUC Louvain
52nd IEEE Conference on Decision and ControlFlorence, Italy – Dec. 2013
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Convex Lyapunov functions
Q: What do we gain/lose by requiring a Lyapunov function to be convex?
Quadratic Lyapunov functions are automatically convex:
Not necessarily true for more complicated Lyapunov functions, e.g., polynomials of higher degree:
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Our setting: switched systems
Uncertain and time-varying nonlinear system:
Interested in: asymptotic stability under arbitrary switching
Special case:
Stability equivalent to “JSR<1”
Joint spectral radius (JSR):
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Agenda for today
1. Computational search for convex polynomial Lyapunov functions
1. sos-convex Lyapunov functions (SDP based)
2. Convexity in analysis of switched linear systems
3. Convexity in analysis of switched nonlinear systems
4. Max-of-convex-polynomial Lyapunov functions and path-complete graphs
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Algorithmic aspects
How to search for convex polynomial Lyapunov functions?
For quadratics is easy: CQLF is a straight LMI…
Higher degree is a different story:
Thm: Deciding whether a degree-4 polynomial is convex isNP-hard in the strong sense.
[AAA, Olshevsky, Parrilo, Tsitsiklis – Math Prog. ‘13]
If can’t decide, certainly can’t optimize
Tractable relaxations?
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Equivalent characterizations of convexity
yxxyxpxpyp T ,))(()()(
yxyxHyT ,0)(
Basic definition:
First order condition:
Second order condition:
Algebraic relaxations: “simply replace ≥ with sos”
All become an SDP
)2
1(]1,0[,
))1(()()1()(
enoughyx
yxpypxp
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Each condition can be SOS-ified
SOSxyxpxpypyxg T ))(()()(),(
SOSyxHyyxg T )(),(2
Basic definition:
First order condition:
Second order condition:
SOSyxpypxpyxg )()()(),(2
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[AAA, Parrilo – SIAM J. on Optimization, ‘13]
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A convex polynomial that is not sos-convex
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30607044
162416335
43254011832)(
xxxxxxx
xxxxxxxxxxx
xxxxxxxxxxxp
[AAA, Parrilo – Math Prog., ’11]
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Convexity = SOS-Convexity?
n,d 2 4 ≥6
1 yes yes yes
2 yes yes no
3 yes no no
≥4 yes no no
n,d 2 4 ≥6
1 yes yes yes
2 yes yes yes
3 yes yes no
≥4 yes no no
Polynomials Forms (homog. polynomials)
[AAA, Parrilo – Math Prog., ’11]
[AAA, Parrilo – SIAM J. on Opt., ‘13]
[AAA, Blekherman, Parrilo – upcoming]
We have a complete characterization:
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SOS-convex Lyapunov functions
Defn: An sos-convex Lyapunov function is a homogenous polynomial V(x) (of even degree) that satisfies:
Notes: Search for V(x) is an SDP More tractable alternatives based on DSOS and SDSOS
programming (LP and SOCP) [AAA, Majumdar’13]
Convex forms are automatically nonnegative SOS-convex forms are sos
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A converse Lyapunov theorem
Notes: Previously known: Convex (norm) Lyapunov functions universal (classical) Polynomial (and sos) Lyapunov functions universal
[Parrilo, Jadbabaie]Proof idea: Approximate norms with convex polynomials Similar construction by Mason et al. in continuous time
In a second step, go from convex to sos-convex
Thm: SOS-convex Lyapunov functions are universal(i.e., necessary and sufficient) for stability.
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Proof idea
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Proof idea
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Proof idea
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Proof idea
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Proof idea
(clearly convex)
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From convex to sos-convex
Thm:convex polynomial Lyapunov function sos-convex Lyapunov function
Proof is algebraic.
This is already an sos-convex polynomial
But need to worry about:
not being sos.This can happen; see [AAA, Parrilo, CDC’11]
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From convex to sos-convex
A recent Positivstellensatz result of Claus Scheiderer:
Thm: (Scheiderer’11) Given any two positive definite forms g and h, there exists an integer k such that g.hk is sos.
Our new Lyapunov funciton:
will be sos
Nice identity:
(for large enough k)
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Polynomial vs. convex polynomial
Thm: The minimum degree of a convex polynomial Lyapunov function can be arbitrarily higher than a non-convex one.
Proof:
Stable for But degree of convex polynomial Lyapunov function∞
as 1 However, a polynomial Lyapunov function of
degree 4 works for all
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Proof
What can the 1-level set of a convex Lyapunov function look like?
How can a non-convex polynomial do this?
Lemma: • If S is invariant, then conv(S) is also invariant.• The Minkowski norm that conv(S) defines is a new
convex (non-polynomial) Lyapunov function.
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Nonlinear switched systems
Lemma: • Unlike the linear case, stability of a switched system on the
corners only does not imply stability of the convex hull.• Unlike the linear case, a common Lyapunov function for the
corners does not imply stability of the convex hull.
Ex. Common Lyapunov function:
But unstable:
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But a convex Lyapunov function implies stability
Suppose we can find a convex common Lyapunov function:
Then, then we have stability of the convex hull.
Proof:
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Example revisited
Ex. Common Lyapunov function:
But unstable:
A different Lyapunov function:
And S is invariant S is part of the ROA under switching
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ROA Computation via SDP
Linearization:
• JSR<1• No polynomial Lyapunov function of degree less than 12• No convex polynomial Lyapunov function of degree less than 14
sos-convex, deg=14
• Left: Cannot make any statements about ROA
• Right: A second SOS problem verifies the largest level set included in the ROA
non-convex, deg=12
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Convex, but beyond polynomials
Parameterization of pointwise maximimum of (sos-convex) Lyapunov functions
[AAA, Jungers, Parrilo, Roozbehani – SIAM J. on Control and Optimization, to appear]
Nodes: Lyapunov functionsEdges: Lyapunov inequalitiesEdge labels: transition maps
Path-completeness: a characterization of all stability-proving LMIsMore general story:
Take any graph whose nodes have incoming edges with all labels
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Thank you for your attention!
Questions?
Want to know more?
http://aaa.lids.mit.edu/