40,988 research outputs found
Extending Hybrid CSP with Probability and Stochasticity
Probabilistic and stochastic behavior are omnipresent in computer controlled
systems, in particular, so-called safety-critical hybrid systems, because of
fundamental properties of nature, uncertain environments, or simplifications to
overcome complexity. Tightly intertwining discrete, continuous and stochastic
dynamics complicates modelling, analysis and verification of stochastic hybrid
systems (SHSs). In the literature, this issue has been extensively
investigated, but unfortunately it still remains challenging as no promising
general solutions are available yet. In this paper, we give our effort by
proposing a general compositional approach for modelling and verification of
SHSs. First, we extend Hybrid CSP (HCSP), a very expressive and process
algebra-like formal modeling language for hybrid systems, by introducing
probability and stochasticity to model SHSs, which is called stochastic HCSP
(SHCSP). To this end, ordinary differential equations (ODEs) are generalized by
stochastic differential equations (SDEs) and non-deterministic choice is
replaced by probabilistic choice. Then, we extend Hybrid Hoare Logic (HHL) to
specify and reason about SHCSP processes. We demonstrate our approach by an
example from real-world.Comment: The conference version of this paper is accepted by SETTA 201
Stochastic hybrid system : modelling and verification
Hybrid systems now form a classical computational paradigm unifying discrete and continuous system aspects. The modelling, analysis and verification of these systems are very difficult.
One way to reduce the complexity of hybrid system models is to consider randomization. The need for stochastic models has actually multiple motivations. Usually, when building models complete information is not available and we have to consider stochastic versions. Moreover, non-determinism and uncertainty are inherent to complex systems. The stochastic approach can be thought of as a way of quantifying non-determinism (by assigning a probability to each
possible execution branch) and managing uncertainty. This is built upon to the - now classical - approach in algorithmics that provides polynomial complexity algorithms via randomization.
In this thesis we investigate the stochastic hybrid systems, focused on modelling and analysis.
We propose a powerful unifying paradigm that combines analytical and formal methods. Its
applications vary from air traffic control to communication networks and healthcare systems.
The stochastic hybrid system paradigm has an explosive development. This is because of its
very powerful expressivity and the great variety of possible applications. Each hybrid system model can be randomized in different ways, giving rise to many classes of stochastic hybrid systems.
Moreover, randomization can change profoundly the mathematical properties of discrete and continuous aspects and also can influence their interaction. Beyond the profound foundational and semantics issues, there is the possibility to combine and cross-fertilize techniques from analytic mathematics (like optimization, control, adaptivity, stability, existence and uniqueness of trajectories, sensitivity analysis) and formal methods (like bisimulation, specification, reachability
analysis, model checking). These constitute the major motivations of our research. We
investigate new models of stochastic hybrid systems and their associated problems. The main difference from the existing approaches is that we do not follow one way (based only on continuous or discrete mathematics), but their cross-fertilization. For stochastic hybrid systems we introduce concepts that have been defined only for discrete transition systems. Then, techniques
that have been used in discrete automata now come in a new analytical fashion. This is partly explained by the fact that popular verification methods (like theorem proving) can hardly work even on probabilistic extensions of discrete systems. When the continuous dimension is added, the idea to use continuous mathematics methods for verification purposes comes in a natural
way.
The concrete contribution of this thesis has four major milestones:
1. A new and a very general model for stochastic hybrid systems;
2. Stochastic reachability for stochastic hybrid systems is introduced together with an approximating method to compute reach set probabilities;
3. Bisimulation for stochastic hybrid systems is introduced and relationship with reachability analysis is investigated.
4. Considering the communication issue, we extend the modelling paradigm
StocHy: automated verification and synthesis of stochastic processes
StocHy is a software tool for the quantitative analysis of discrete-time
stochastic hybrid systems (SHS). StocHy accepts a high-level description of
stochastic models and constructs an equivalent SHS model. The tool allows to
(i) simulate the SHS evolution over a given time horizon; and to automatically
construct formal abstractions of the SHS. Abstractions are then employed for
(ii) formal verification or (iii) control (policy, strategy) synthesis. StocHy
allows for modular modelling, and has separate simulation, verification and
synthesis engines, which are implemented as independent libraries. This allows
for libraries to be easily used and for extensions to be easily built. The tool
is implemented in C++ and employs manipulations based on vector calculus, the
use of sparse matrices, the symbolic construction of probabilistic kernels, and
multi-threading. Experiments show StocHy's markedly improved performance when
compared to existing abstraction-based approaches: in particular, StocHy beats
state-of-the-art tools in terms of precision (abstraction error) and
computational effort, and finally attains scalability to large-sized models (12
continuous dimensions). StocHy is available at www.gitlab.com/natchi92/StocHy
Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance
Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft
or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner.
Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''.
The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few.
This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage.
The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling
Cyber-Virtual Systems: Simulation, Validation & Visualization
We describe our ongoing work and view on simulation, validation and
visualization of cyber-physical systems in industrial automation during
development, operation and maintenance. System models may represent an existing
physical part - for example an existing robot installation - and a software
simulated part - for example a possible future extension. We call such systems
cyber-virtual systems.
In this paper, we present the existing VITELab infrastructure for
visualization tasks in industrial automation. The new methodology for
simulation and validation motivated in this paper integrates this
infrastructure. We are targeting scenarios, where industrial sites which may be
in remote locations are modeled and visualized from different sites anywhere in
the world.
Complementing the visualization work, here, we are also concentrating on
software modeling challenges related to cyber-virtual systems and simulation,
testing, validation and verification techniques for them. Software models of
industrial sites require behavioural models of the components of the industrial
sites such as models for tools, robots, workpieces and other machinery as well
as communication and sensor facilities. Furthermore, collaboration between
sites is an important goal of our work.Comment: Preprint, 9th International Conference on Evaluation of Novel
Approaches to Software Engineering (ENASE 2014
Anytime system level verification via parallel random exhaustive hardware in the loop simulation
System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems.
We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability).
We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability
The Construction of Verification Models for Embedded Systems
The usefulness of verification hinges on the quality of the verification model. Verification is useful if it increases our confidence that an artefact bahaves as expected. As modelling inherently contains non-formal elements, the qualityof models cannot be captured by purely formal means. Still, we argue that modelling is not an act of irrationalism and unpredictable geniality, but follows rational arguments, that often remain implicit. In this paper we try to identify the tacit rationalism in the model construction as performed by most people doing modelling for verification. By explicating the different phases, arguments, and design decisions in the model construction, we try to develop guidelines that help to improve the process of model construction and the quality of models
Model checking embedded system designs
We survey the basic principles behind the application of model checking to controller verification and synthesis. A promising development is the area of guided model checking, in which the state space search strategy of the model checking algorithm can be influenced to visit more interesting sets of states first. In particular, we discuss how model checking can be combined with heuristic cost functions to guide search strategies. Finally, we list a number of current research developments, especially in the area of reachability analysis for optimal control and related issues
Bisimulation Relations Between Automata, Stochastic Differential Equations and Petri Nets
Two formal stochastic models are said to be bisimilar if their solutions as a
stochastic process are probabilistically equivalent. Bisimilarity between two
stochastic model formalisms means that the strengths of one stochastic model
formalism can be used by the other stochastic model formalism. The aim of this
paper is to explain bisimilarity relations between stochastic hybrid automata,
stochastic differential equations on hybrid space and stochastic hybrid Petri
nets. These bisimilarity relations make it possible to combine the formal
verification power of automata with the analysis power of stochastic
differential equations and the compositional specification power of Petri nets.
The relations and their combined strengths are illustrated for an air traffic
example.Comment: 15 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA),
EPTCS 20m 201
Model checking learning agent systems using Promela with embedded C code and abstraction
As autonomous systems become more prevalent, methods for their verification will become more
widely used. Model checking is a formal verification technique that can help ensure the safety of autonomous
systems, but in most cases it cannot be applied by novices, or in its straight \off-the-shelf" form. In order
to be more widely applicable it is crucial that more sophisticated techniques are used, and are presented
in a way that is reproducible by engineers and verifiers alike. In this paper we demonstrate in detail two
techniques that are used to increase the power of model checking using the model checker SPIN. The first
of these is the use of embedded C code within Promela specifications, in order to accurately re
ect robot
movement. The second is to use abstraction together with a simulation relation to allow us to verify multiple
environments simultaneously. We apply these techniques to a fairly simple system in which a robot moves
about a fixed circular environment and learns to avoid obstacles. The learning algorithm is inspired by the
way that insects learn to avoid obstacles in response to pain signals received from their antennae. Crucially,
we prove that our abstraction is sound for our example system { a step that is often omitted but is vital if
formal verification is to be widely accepted as a useful and meaningful approach
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