291 research outputs found
Reachability of Uncertain Linear Systems Using Zonotopes
International audienceWe present a method for the computation of reachable sets of uncertain linear systems. The main innovation of the method consists in the use of zonotopes for reachable set representation. Zonotopes are special polytopes with several interesting properties : they can be encoded efficiently, they are closed under linear transformations and Minkowski sum. The resulting method has been used to treat several examples and has shown great performances for high dimensional systems. An extension of the method for the verification of piecewise linear hybrid systems is proposed
Reachability analysis of linear hybrid systems via block decomposition
Reachability analysis aims at identifying states reachable by a system within
a given time horizon. This task is known to be computationally expensive for
linear hybrid systems. Reachability analysis works by iteratively applying
continuous and discrete post operators to compute states reachable according to
continuous and discrete dynamics, respectively. In this paper, we enhance both
of these operators and make sure that most of the involved computations are
performed in low-dimensional state space. In particular, we improve the
continuous-post operator by performing computations in high-dimensional state
space only for time intervals relevant for the subsequent application of the
discrete-post operator. Furthermore, the new discrete-post operator performs
low-dimensional computations by leveraging the structure of the guard and
assignment of a considered transition. We illustrate the potential of our
approach on a number of challenging benchmarks.Comment: Accepted at EMSOFT 202
Efficient reachability analysis of parametric linear hybrid systems with time-triggered transitions
Efficiently handling time-triggered and possibly nondeterministic switches
for hybrid systems reachability is a challenging task. In this paper we present
an approach based on conservative set-based enclosure of the dynamics that can
handle systems with uncertain parameters and inputs, where the uncertainties
are bound to given intervals. The method is evaluated on the plant model of an
experimental electro-mechanical braking system with periodic controller. In
this model, the fast-switching controller dynamics requires simulation time
scales of the order of nanoseconds. Accurate set-based computations for
relatively large time horizons are known to be expensive. However, by
appropriately decoupling the time variable with respect to the spatial
variables, and enclosing the uncertain parameters using interval matrix maps
acting on zonotopes, we show that the computation time can be lowered to 5000
times faster with respect to previous works. This is a step forward in formal
verification of hybrid systems because reduced run-times allow engineers to
introduce more expressiveness in their models with a relatively inexpensive
computational cost.Comment: Submitte
Reach Set Approximation through Decomposition with Low-dimensional Sets and High-dimensional Matrices
Approximating the set of reachable states of a dynamical system is an
algorithmic yet mathematically rigorous way to reason about its safety.
Although progress has been made in the development of efficient algorithms for
affine dynamical systems, available algorithms still lack scalability to ensure
their wide adoption in the industrial setting. While modern linear algebra
packages are efficient for matrices with tens of thousands of dimensions,
set-based image computations are limited to a few hundred. We propose to
decompose reach set computations such that set operations are performed in low
dimensions, while matrix operations like exponentiation are carried out in the
full dimension. Our method is applicable both in dense- and discrete-time
settings. For a set of standard benchmarks, it shows a speed-up of up to two
orders of magnitude compared to the respective state-of-the art tools, with
only modest losses in accuracy. For the dense-time case, we show an experiment
with more than 10.000 variables, roughly two orders of magnitude higher than
possible with previous approaches
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