1,575 research outputs found
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks
In this paper, the output reachable estimation and safety verification
problems for multi-layer perceptron neural networks are addressed. First, a
conception called maximum sensitivity in introduced and, for a class of
multi-layer perceptrons whose activation functions are monotonic functions, the
maximum sensitivity can be computed via solving convex optimization problems.
Then, using a simulation-based method, the output reachable set estimation
problem for neural networks is formulated into a chain of optimization
problems. Finally, an automated safety verification is developed based on the
output reachable set estimation result. An application to the safety
verification for a robotic arm model with two joints is presented to show the
effectiveness of proposed approaches.Comment: 8 pages, 9 figures, to appear in TNNL
Forward Invariant Cuts to Simplify Proofs of Safety
The use of deductive techniques, such as theorem provers, has several
advantages in safety verification of hybrid sys- tems; however,
state-of-the-art theorem provers require ex- tensive manual intervention.
Furthermore, there is often a gap between the type of assistance that a theorem
prover requires to make progress on a proof task and the assis- tance that a
system designer is able to provide. This paper presents an extension to
KeYmaera, a deductive verification tool for differential dynamic logic; the new
technique allows local reasoning using system designer intuition about per-
formance within particular modes as part of a proof task. Our approach allows
the theorem prover to leverage for- ward invariants, discovered using numerical
techniques, as part of a proof of safety. We introduce a new inference rule
into the proof calculus of KeYmaera, the forward invariant cut rule, and we
present a methodology to discover useful forward invariants, which are then
used with the new cut rule to complete verification tasks. We demonstrate how
our new approach can be used to complete verification tasks that lie out of the
reach of existing deductive approaches us- ing several examples, including one
involving an automotive powertrain control system.Comment: Extended version of EMSOFT pape
A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics
We consider the problem of extracting safe environments and controllers for
reach-avoid objectives for systems with known state and control spaces, but
unknown dynamics. In a given environment, a common approach is to synthesize a
controller from an abstraction or a model of the system (potentially learned
from data). However, in many situations, the relationship between the dynamics
of the model and the \textit{actual system} is not known; and hence it is
difficult to provide safety guarantees for the system. In such cases, the
Standard Simulation Metric (SSM), defined as the worst-case norm distance
between the model and the system output trajectories, can be used to modify a
reach-avoid specification for the system into a more stringent specification
for the abstraction. Nevertheless, the obtained distance, and hence the
modified specification, can be quite conservative. This limits the set of
environments for which a safe controller can be obtained. We propose SPEC, a
specification-centric simulation metric, which overcomes these limitations by
computing the distance using only the trajectories that violate the
specification for the system. We show that modifying a reach-avoid
specification with SPEC allows us to synthesize a safe controller for a larger
set of environments compared to SSM. We also propose a probabilistic method to
compute SPEC for a general class of systems. Case studies using simulators for
quadrotors and autonomous cars illustrate the advantages of the proposed metric
for determining safe environment sets and controllers.Comment: 22nd ACM International Conference on Hybrid Systems: Computation and
Control (2019
Approximating Reachable Sets for Neural Network based Models in Real-Time via Optimal Control
In this paper, we present a data-driven framework for real-time estimation of
reachable sets for control systems where the plant is modeled using neural
networks (NNs). We utilize a running example of a quadrotor model that is
learned using trajectory data via NNs. The NN learned offline, can be excited
online to obtain linear approximations for reachability analysis. We use a
dynamic mode decomposition based approach to obtain linear liftings of the NN
model. The linear models thus obtained can utilize optimal control theory to
obtain polytopic approximations to the reachable sets in real-time. The
polytopic approximations can be tuned to arbitrary degrees of accuracy. The
proposed framework can be extended to other nonlinear models that utilize NNs
to estimate plant dynamics. We demonstrate the effectiveness of the proposed
framework using an illustrative simulation of quadrotor dynamics.Comment: 14 pages, 11 figures, journal paper that has been conditionally
accepte
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