715 research outputs found
An Analytical Framework for Control Synthesis of Cyber-Physical Systems with Safety Guarantee
Cyber-physical systems (CPS) are required to operate safely under fault and
malicious attacks. The simplex architecture and the recently proposed cyber
resilient architectures, e.g., Byzantine fault tolerant++ (BFT++), provide
safety for CPS under faults and malicious cyber attacks, respectively. However,
these existing architectures make use of different timing parameters and
implementations to provide safety, and are seemingly unrelated. In this paper,
we propose an analytical framework to represent the simplex, BFT++ and other
practical cyber resilient architectures (CRAs). We construct a hybrid system
that models CPS adopting any of these architectures. We derive sufficient
conditions via our proposed framework under which a control policy is
guaranteed to be safe. We present an algorithm to synthesize the control
policy. We validate the proposed framework using a case study on lateral
control of a Boeing 747, and demonstrate that our proposed approach ensures
safety of the system
Business intelligence to improve the quality of local government services : case-study in a local government Town Hall
The use of business intelligence (BI) systems by organizations is increasingly considered as an asset, which goal is to provide access to information in a timely manner in order to support the decision-making process. However, in specific cases such as local government organizations, there are very specific challenges. Some of them like privacy rights and applicable law compliance must be carefully observed, making the necessary adaptations of these BI solutions. The developed solution brings some important contributions and represents some advances in the eGovernment context applied to local governments where the information is normally used/stored are not normalized and pre-defined. Being this a big barrier to development of this type of solutions, the developed architecture is prepared to improve the data quality and avoid this type of mistakes. This paper presents an architecture of a BI platform on a local government organization, geared towards the improvement of citizen offered services quality and efficiency maximization, thus contributing for cost reduction to the taxpayer.(undefined
A predictive safety filter for learning-based racing control
The growing need for high-performance controllers in safety-critical
applications like autonomous driving has been motivating the development of
formal safety verification techniques. In this paper, we design and implement a
predictive safety filter that is able to maintain vehicle safety with respect
to track boundaries when paired alongside any potentially unsafe control
signal, such as those found in learning-based methods. A model predictive
control (MPC) framework is used to create a minimally invasive algorithm that
certifies whether a desired control input is safe and can be applied to the
vehicle, or that provides an alternate input to keep the vehicle in bounds. To
this end, we provide a principled procedure to compute a safe and invariant set
for nonlinear dynamic bicycle models using efficient convex approximation
techniques. To fully support an aggressive racing performance without
conservative safety interventions, the safe set is extended in real-time
through predictive control backup trajectories. Applications for assisted
manual driving and deep imitation learning on a miniature remote-controlled
vehicle demonstrate the safety filter's ability to ensure vehicle safety during
aggressive maneuvers
Using control synthesis to generate corner cases: A case study on autonomous driving
This paper employs correct-by-construction control synthesis, in particular
controlled invariant set computations, for falsification. Our hypothesis is
that if it is possible to compute a "large enough" controlled invariant set
either for the actual system model or some simplification of the system model,
interesting corner cases for other control designs can be generated by sampling
initial conditions from the boundary of this controlled invariant set.
Moreover, if falsifying trajectories for a given control design can be found
through such sampling, then the controlled invariant set can be used as a
supervisor to ensure safe operation of the control design under consideration.
In addition to interesting initial conditions, which are mostly related to
safety violations in transients, we use solutions from a dual game, a
reachability game for the safety specification, to find falsifying inputs. We
also propose optimization-based heuristics for input generation for cases when
the state is outside the winning set of the dual game. To demonstrate the
proposed ideas, we consider case studies from basic autonomous driving
functionality, in particular, adaptive cruise control and lane keeping. We show
how the proposed technique can be used to find interesting falsifying
trajectories for classical control designs like proportional controllers,
proportional integral controllers and model predictive controllers, as well as
an open source real-world autonomous driving package.Comment: To appear at EMSOFT 201
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