39 research outputs found
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
Sapo: Reachability Computation and Parameter Synthesis of Polynomial Dynamical Systems
Sapo is a C++ tool for the formal analysis of polynomial dynamical systems.
Its main features are: 1) Reachability computation, i.e., the calculation of
the set of states reachable from a set of initial conditions, and 2) Parameter
synthesis, i.e., the refinement of a set of parameters so that the system
satisfies a given specification. Sapo can represent reachable sets as unions of
boxes, parallelotopes, or parallelotope bundles (symbolic representation of
polytopes). Sets of parameters are represented with polytopes while
specifications are formalized as Signal Temporal Logic (STL) formulas
Reachability Analysis for Neural Feedback Systems Using Regressive Polynomial Rule Inference
We present an approach to construct reachable set overapproxi- mations for continuous-time dynamical systems controlled using neural network feedback systems. Feedforward deep neural net- works are now widely used as a means for learning control laws through techniques such as reinforcement learning and data-driven predictive control. However, the learning algorithms for these net- works do not guarantee correctness properties on the resulting closed-loop systems. Our approach seeks to construct overapproxi- mate reachable sets by integrating a Taylor model-based flowpipe construction scheme for continuous differential equations with an approach that replaces the neural network feedback law for a small subset of inputs by a polynomial mapping. We generate the polynomial mapping using regression from input-output sam- ples. To ensure soundness, we rigorously quantify the gap between the output of the network and that of the polynomial model. We demonstrate the effectiveness of our approach over a suite of bench- mark examples ranging from 2 to 17 state variables, comparing our approach with alternative ideas based on range analysis
Reachability computation for polynomial dynamical systems
This paper is concerned with the problem of computing the bounded time reachable set of a polynomial discrete-time dynamical system. The problem is well-known for being difficult when nonlinear systems are considered. In this regard, we propose three reachability methods that differ in the set representation. The proposed algorithms adopt boxes, parallelotopes, and parallelotope bundles to construct flowpipes that contain the actual reachable sets. The latter is a new data structure for the symbolic representation of polytopes. Our methods exploit the Bernstein expansion of polynomials to bound the images of sets. The scalability and precision of the presented methods are analyzed on a number of dynamical systems, in comparison with other existing approaches
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
Data-Driven Reachability Analysis of Stochastic Dynamical Systems with Conformal Inference
We consider data-driven reachability analysis of discrete-time stochastic
dynamical systems using conformal inference. We assume that we are not provided
with a symbolic representation of the stochastic system, but instead have
access to a dataset of -step trajectories. The reachability problem is to
construct a probabilistic flowpipe such that the probability that a -step
trajectory can violate the bounds of the flowpipe does not exceed a
user-specified failure probability threshold. The key ideas in this paper are:
(1) to learn a surrogate predictor model from data, (2) to perform reachability
analysis using the surrogate model, and (3) to quantify the surrogate model's
incurred error using conformal inference in order to give probabilistic
reachability guarantees. We focus on learning-enabled control systems with
complex closed-loop dynamics that are difficult to model symbolically, but
where state transition pairs can be queried, e.g., using a simulator. We
demonstrate the applicability of our method on examples from the domain of
learning-enabled cyber-physical systems
Compositional Taylor model based validated integration
Validated integration is a family of methods that compute enclosures for sets of initial conditions in the Initial Value Problems. The Taylor model based validated integration methods use truncated Taylor series to approximate the solution to the Initial Value Problem and often give better results than other validated integration methods. Validated integration methods, and especially Taylor model based ones, become increasingly more impractical as the number of variables in the system get higher.
In this thesis, we develop techniques that mitigate the issues related to the dimension of the system in Taylor model based validated integration methods. This is done by taking advantage of the compositional structure of the problem when possible. More precisely, the main contribution of this thesis is to enable computing an enclosure to a higher dimensional system by using enclosures for smaller lower dimensional subsystem that are contained in the larger system.
The techniques called shrink wrapping and preconditioning are used in the Taylor model based validated integration to improve accuracy. We also analyse these techniques from a compositional viewpoint and present their compositional counterparts.
We accompany compositional version of the Taylor model based validated integration with implementation of our tool CFlow* and experiments using our tool. The experimental results show performance gains for some systems with non-trivial compositional structure.
This work was motivated by interest in formally analysing biological systems and we use biological systems examples in a number of our systems
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for a small subset of inputs. To quantify the error introduced by abstraction, we provide both theoretical error bound estimation based on the theory of Bernstein polynomials and more practical sampling based error bound estimation, following a tight Lipschitz constant estimation approach based on forward reachability analysis. Compared with previous methods, our approach addresses a much broader set of neural networks, including heterogeneous neural networks that contain multiple types of activation functions. Experiment results on a variety of benchmarks show the effectiveness of our approach