1,154 research outputs found
Approximately bisimilar symbolic models for nonlinear control systems
Control systems are usually modeled by differential equations describing how
physical phenomena can be influenced by certain control parameters or inputs.
Although these models are very powerful when dealing with physical phenomena,
they are less suitable to describe software and hardware interfacing the
physical world. For this reason there is a growing interest in describing
control systems through symbolic models that are abstract descriptions of the
continuous dynamics, where each "symbol" corresponds to an "aggregate" of
states in the continuous model. Since these symbolic models are of the same
nature of the models used in computer science to describe software and
hardware, they provide a unified language to study problems of control in which
software and hardware interact with the physical world. Furthermore the use of
symbolic models enables one to leverage techniques from supervisory control and
algorithms from game theory for controller synthesis purposes. In this paper we
show that every incrementally globally asymptotically stable nonlinear control
system is approximately equivalent (bisimilar) to a symbolic model. The
approximation error is a design parameter in the construction of the symbolic
model and can be rendered as small as desired. Furthermore if the state space
of the control system is bounded the obtained symbolic model is finite. For
digital control systems, and under the stronger assumption of incremental
input-to-state stability, symbolic models can be constructed through a suitable
quantization of the inputs.Comment: Corrected typo
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
Monitoring with uncertainty
We discuss the problem of runtime verification of an instrumented program
that misses to emit and to monitor some events. These gaps can occur when a
monitoring overhead control mechanism is introduced to disable the monitor of
an application with real-time constraints. We show how to use statistical
models to learn the application behavior and to "fill in" the introduced gaps.
Finally, we present and discuss some techniques developed in the last three
years to estimate the probability that a property of interest is violated in
the presence of an incomplete trace.Comment: In Proceedings HAS 2013, arXiv:1308.490
Symbolic models for nonlinear control systems without stability assumptions
Finite-state models of control systems were proposed by several researchers
as a convenient mechanism to synthesize controllers enforcing complex
specifications. Most techniques for the construction of such symbolic models
have two main drawbacks: either they can only be applied to restrictive classes
of systems, or they require the exact computation of reachable sets. In this
paper, we propose a new abstraction technique that is applicable to any smooth
control system as long as we are only interested in its behavior in a compact
set. Moreover, the exact computation of reachable sets is not required. The
effectiveness of the proposed results is illustrated by synthesizing a
controller to steer a vehicle.Comment: 11 pages, 2 figures, journa
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