672 research outputs found
A Converse Sum of Squares Lyapunov Result with a Degree Bound
Sum of Squares programming has been used extensively over the past decade for
the stability analysis of nonlinear systems but several questions remain
unanswered. In this paper, we show that exponential stability of a polynomial
vector field on a bounded set implies the existence of a Lyapunov function
which is a sum-of-squares of polynomials. In particular, the main result states
that if a system is exponentially stable on a bounded nonempty set, then there
exists an SOS Lyapunov function which is exponentially decreasing on that
bounded set. The proof is constructive and uses the Picard iteration. A bound
on the degree of this converse Lyapunov function is also given. This result
implies that semidefinite programming can be used to answer the question of
stability of a polynomial vector field with a bound on complexity
Lower Bounds on Complexity of Lyapunov Functions for Switched Linear Systems
We show that for any positive integer , there are families of switched
linear systems---in fixed dimension and defined by two matrices only---that are
stable under arbitrary switching but do not admit (i) a polynomial Lyapunov
function of degree , or (ii) a polytopic Lyapunov function with facets, or (iii) a piecewise quadratic Lyapunov function with
pieces. This implies that there cannot be an upper bound on the size of the
linear and semidefinite programs that search for such stability certificates.
Several constructive and non-constructive arguments are presented which connect
our problem to known (and rather classical) results in the literature regarding
the finiteness conjecture, undecidability, and non-algebraicity of the joint
spectral radius. In particular, we show that existence of an extremal piecewise
algebraic Lyapunov function implies the finiteness property of the optimal
product, generalizing a result of Lagarias and Wang. As a corollary, we prove
that the finiteness property holds for sets of matrices with an extremal
Lyapunov function belonging to some of the most popular function classes in
controls
Linearly Solvable Stochastic Control Lyapunov Functions
This paper presents a new method for synthesizing stochastic control Lyapunov
functions for a class of nonlinear stochastic control systems. The technique
relies on a transformation of the classical nonlinear Hamilton-Jacobi-Bellman
partial differential equation to a linear partial differential equation for a
class of problems with a particular constraint on the stochastic forcing. This
linear partial differential equation can then be relaxed to a linear
differential inclusion, allowing for relaxed solutions to be generated using
sum of squares programming. The resulting relaxed solutions are in fact
viscosity super/subsolutions, and by the maximum principle are pointwise upper
and lower bounds to the underlying value function, even for coarse polynomial
approximations. Furthermore, the pointwise upper bound is shown to be a
stochastic control Lyapunov function, yielding a method for generating
nonlinear controllers with pointwise bounded distance from the optimal cost
when using the optimal controller. These approximate solutions may be computed
with non-increasing error via a hierarchy of semidefinite optimization
problems. Finally, this paper develops a-priori bounds on trajectory
suboptimality when using these approximate value functions, as well as
demonstrates that these methods, and bounds, can be applied to a more general
class of nonlinear systems not obeying the constraint on stochastic forcing.
Simulated examples illustrate the methodology.Comment: Published in SIAM Journal of Control and Optimizatio
Region of Attraction Estimation Using Invariant Sets and Rational Lyapunov Functions
This work addresses the problem of estimating the region of attraction (RA)
of equilibrium points of nonlinear dynamical systems. The estimates we provide
are given by positively invariant sets which are not necessarily defined by
level sets of a Lyapunov function. Moreover, we present conditions for the
existence of Lyapunov functions linked to the positively invariant set
formulation we propose. Connections to fundamental results on estimates of the
RA are presented and support the search of Lyapunov functions of a rational
nature. We then restrict our attention to systems governed by polynomial vector
fields and provide an algorithm that is guaranteed to enlarge the estimate of
the RA at each iteration
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