Faglige interesser
 CyberPhysical systems
 Networked control
 Connected and intelligent transportation systems
 Hybrid systems: modelling, simulation, design and validation
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ITD25018 Teknologiprosjekt
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https://scholar.google.com/citations?user=VrWL7wAAAAJ&hl=no&oi=ao
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Samarbeid
Forskergrupper
Emneord:
CyberPhysical Systems,
Networked control,
Hybrid systems
Publikasjoner

Bochaova, Irina; Kudryashov, Boris D.; Rabi, Maben; Lyamin, Nikita; Dankers, Wouter; Erik, Frick & Vinel, Alexey (2019). Characterizing Packet Losses in Vehicular Networks. IEEE Transactions on Vehicular Technology.
ISSN 00189545.
68(9), s 8347 8358 . doi:
10.1109/TVT.2019.2930689
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To enable testing and performance evaluation of new connected and autonomous driving functions, it is important to characterize packet losses caused by degradation in vehicular (V2X) communication channels. In this paper we suggest an approach to constructing packet loss models based on the socalled PseudoMarkov chains (PMC). The PMCbased model needs only short training sequences, has low computational complexity, and yet provides more precise approximations than known techniques. We show how to learn PMC models from either empirical records of packet receptions, or from analytical models of fluctuations in the received signal strength. In particular, we validate our approach by applying it on: 1) V2X packet reception data collected from an active safety test run, which used the LTE network of the AstaZero automotive testing site in Sweden, and 2) variants of the Rician fading channel models corresponding to two models of correlations of packet losses. We also show that initializing the BaumWelch algorithm with a second order PMC model leads to a high accuracy model.

Rabi, Maben; Ramesh, Chithrupa & Karl, Johansson (2016). Separated Design of Encoder and Controller for Networked Linear Quadratic Optimal Control. SIAM Journal of Control and Optimization.
ISSN 03630129.
54(2), s 662 689 . doi:
10.1137/14M0970987
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For a networked control system, we consider the problem of encoder and controller design. We study a discretetime linear plant with a finite horizon performance cost, comprising a quadratic function of the states and controls, and an additive communication cost. We study separation in design of the encoder and controller, along with related closedloop properties such as the dual effect and certainty equivalence. The encoder outputs are quantized samples, but our results also apply to two other formats for encoder outputs: realvalued samples at eventtriggered times, and realvalued samples over additive noise channels. If the controller and encoder are dynamic, then we show that the performance cost is minimized by a separated design: the controls are updated at each time instant as per a certainty equivalence law, and the encoder is chosen to minimize an aggregate quadratic distortion of the estimation error. This separation is shown to hold even though a dual effect is present in the closedloop system. We also show that this separated design need not be optimal when the controller or encoder are to be chosen from within restricted classes.

Rabi, Maben; George, Moustakides & Baras, John (2012). Adaptive Sampling for Linear State Estimation. SIAM Journal of Control and Optimization.
ISSN 03630129.
50(2), s 672 702 . doi:
10.1137/090757125
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When a sensor has continuous measurements but sends occasional messages over a data network to a supervisor which estimates the state, the available packet rate fixes the achievable quality of state estimation. When such rate limits turn stringent, the sensor's messaging policy should be designed anew. What are good causal messaging policies? What should message packets contain? What is the lowest possible distortion in a causal estimate at the supervisor? Is Delta sampling better than periodic sampling? We answer these questions for a Markov state process under an idealized model of the network and the assumption of perfect state measurements at the sensor. If the state is a scalar, or a vector of low dimension, then we can ignore sample quantization. If in addition we can ignore jitter in the transmission delays over the network, then our search for efficient messaging policies simplifies. First, each message packet should contain the value of the state at that time. Thus a bound on the number of data packets becomes a bound on the number of state samples. Second, the remaining choice in messaging is entirely about the times when samples are taken. For a scalar, linear diffusion process, we study the problem of choosing the causal sampling times that will give the lowest aggregate squared error distortion. We stick to finite horizons and impose a hard upper bound N on the number of allowed samples. We cast the design as a problem of choosing an optimal sequence of stopping times. We reduce this to a nested sequence of problems, each asking for a single optimal stopping time. Under an unproven but natural assumption about the leastsquare estimate at the supervisor, each of these single stopping problems are of standard form. The optimal stopping times are random times when the estimation error exceeds designed envelopes. For the case where the state is a Brownian motion, we give analytically: the shape of the optimal sampling envelopes, the shape of the envelopes under optimal Delta sampling, and their performances. Surprisingly, we find that Delta sampling performs badly. Hence, when the rate constraint is a hard limit on the number of samples over a finite horizon, we should not use Delta sampling.

Rabi, Maben; Stabellini, Luca; Proutiere, Alexandre & Johansson, Mikael (2010). Networked estimation under contention‐based medium access. International Journal of Robust and Nonlinear Control.
ISSN 10498923.
20(2), s 140 155 . doi:
10.1002/rnc.1459
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This paper studies networked estimation over a communication channel shared by a contention‐based medium access protocol. A collection of N identical and physically decoupled scalar systems are sampled without sensor noise and transmitted over a common channel, using a contention‐based medium access mechanism. We first carry out a calculation of the average distortion in estimation with irregular samples. Given the rate of packet generation at sensors, we characterize the traffic characteristics of the some contention‐based MAC schemes. This lets us derive the statistics of inter‐arrival times which in turn allows us to compute the packet loss rates and also the statistics of delay within a sample period. Using these results, we track the estimation performance as the sample generation rate and the number of contending nodes are varied. We provide a heuristic rule‐of‐thumb for choosing the sampling interval which minimizes the average distortion. By combining the network traffic characterization with that of the estimation performance, we show this rule performs pretty well. Carrying along the same lines, we are able to compute the scaling limits of estimation performance with respect to the number of contending nodes.

Johansson, Björn; Rabi, Maben & Johansson, Mikael (2009). A Randomized Incremental Subgradient Method for Distributed Optimization in Networked Systems. SIAM Journal on Optimization.
ISSN 10526234.
20(3), s 1157 1170 . doi:
10.1137/08073038X
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We present an algorithm that generalizes the randomized incremental subgradient method with fixed stepsize due to Nedić and Bertsekas [SIAM J. Optim., 12 (2001), pp. 109–138]. Our novel algorithm is particularly suitable for distributed implementation and execution, and possible applications include distributed optimization, e.g., parameter estimation in networks of tiny wireless sensors. The stochastic component in the algorithm is described by a Markov chain, which can be constructed in a distributed fashion using only local information. We provide a detailed convergence analysis of the proposed algorithm and compare it with existing, both deterministic and randomized, incremental subgradient methods.
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Haugen, Øystein & Rabi, Maben (2019). Internet of Things.

Rabi, Maben & Johansson, Karl (2009). Scheduling packets for Eventtriggered control.
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For a single control loop with communication rate constraints, Eventtriggered control often outperforms periodic control. When multiple loops are being controlled over a shared contentionbased medium, however, the advantage of eventtriggered policies is less well understood. In this paper, we consider eventtriggered impulse control under lossy communication. The sampling events are determined by level crossings of the plant output. It is shown how a stochastic control criterion depends on the level thresholds and the packet loss probability for a class of integrator plants. For multiple control loops, this result is used to derive a design guideline on how to assign the levels that lead to optimal use of the available communication resources. It is shown that the structure of the event generator depends critically on the loss probability.

Rabi, Maben & Johansson, Karl (2008). Optimal stopping for eventtriggered sensing and actuation.
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Novel eventtriggered sensing and actuation strategies are presented for networked control systems with limited communication resources. Two architectures are considered: one with the controller colocated with the sensor and one with the control colocated with the actuator. A stochastic control problem with an optimal stopping rule is shown to capture two interesting instances of these architectures. The solution of the problem leads to a parametrization of the control alphabet as piecewise constant commands. The execution of the control commands is triggered by stopping rules for the sensor. In simple situations, it is possible to analytically derive the optimal controller. Examples illustrate how the new eventbased control and sensing strategies outperform conventional timetriggered schemes.

Rabi, Maben & Baras, John (2005). Maximum Entropy Models, Dynamic Games, and Robust Output Feedback Control for Automata.
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n this paper, we develop a framework for designing controllers for automata which are robust with respect to uncertainties. A deterministic model for uncertainties is introduced, leading to a dynamic game formulation of the robust control problem. This problem is solved using an appropriate information state. We derive a Hidden Markov Model as the maximum entropy stochastic model for the automaton. A risksensitive stochastic control problem is formulated and solved for this Hidden Markov Model. The two problems are related using small noise limits.

Rabi, Maben & Baras, John (2002). Intrusion Detection with Support Vector Machines and Generative Models.
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This paper addresses the taskof detecting intrusions in the form of malicious attacks on programs running on a host computer system by inspecting the trace of system calls made by these programs. We use ‘attacktree’ type generative models for such intrusions to select features that are used by a Support Vector Machine Classifier. Our approach combines the ability of an HMM generative model to handle variablelength strings, i.e. the traces, and the nonasymptotic nature of Support Vector Machines that permits them to work well with small training sets.
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Publisert 13. juni 2019 14:52
 Sist endret 10. sep. 2019 09:56