Pappas, George J

Email Address
Research Projects
Organizational Units
Research Interests

Search Results

Now showing 1 - 10 of 121
  • Publication
    Joint Metering and Conflict Resolution in Air Traffic Control
    (2010-11-28) Le Ny, Jerome; Pappas, George J
    This paper describes a novel optimization-based approach to conflict resolution in air traffic control, based on geometric programming. The main advantage of the approach is that Geometric Programs (GPs) can also capture various metering directives issued by the traffic flow management level, in contrast to most recent methods focusing purely on aircraft separation issues. GPs can also account for some of the nonlinearities present in the formulations of conflict resolution problems, while incurring only a small penalty in computation time with respect to the fastest linear programming based approaches. Additional integer variables can be introduced to improve the quality of the obtained solutions and handle combinatorial choices, resulting in Mixed-Integer Geometric Programs (MIGPs). We present GPs and MIGPs to solve a variety of joint metering and separation scenarios, e.g. including miles-in-trail and minutes-in-trail restrictions through airspace fixes and boundaries. Simulation results demonstrate the efficiency of the approach.
  • Publication
    Estimation of Blood Oxygen Content Using Context-Aware Filtering
    (2016-04-01) Ivanov, Radoslav; Atanasov, Nikolay; Weimer, James; Simpao, Allan F; Rehman, Mohamed A; Pappas, George; Lee, Insup; Pajic, Miroslav
    In this paper we address the problem of estimating the blood oxygen concentration in children during surgery.Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children’s Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables.
  • Publication
    Attack-Resilient State Estimation in the Presence of Noise
    (2015-12-01) Pajic, Miroslav; Lee, Insup; Tabuada, Paulo; Pappas, George
    We consider the problem of attack-resilient state estimation in the presence of noise. We focus on the most general model for sensor attacks where any signal can be injected via the compromised sensors. An l0-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the l1 norm are presented. For both l0 and l1-based state estimators, we derive rigorous analytic bounds on the state-estimation errors. We show that the worst-case error is linear with the size of the noise, meaning that the attacker cannot exploit noise and modeling errors to introduce unbounded state-estimation errors. Finally, we show how the presented attack-resilient state estimators can be used for sound attack detection and identification, and provide conditions on the size of attack vectors that will ensure correct identification of compromised sensors.
  • Publication
    Robust Estimation Using Context-Aware Filtering
    (2015-09-01) Ivanov, Radoslav; Atanasov, Nikolay; Pappas, George; Lee, Insup; Pajic, Miroslav
    This paper presents the context-aware filter, an estimation technique that incorporates context measurements, in addition to the regular continuous measurements. Context measurements provide binary information about the system’s context which is not directly encoded in the state; examples include a robot detecting a nearby building using image processing or a medical device alarming that a vital sign has exceeded a predefined threshold. These measurements can only be received from certain states and can therefore be modeled as a function of the system’s current state. We focus on two classes of functions describing the probability of context detection given the current state; these functions capture a wide variety of detections that may occur in practice. We derive the corresponding context-aware filters, a Gaussian Mixture filter and another closed-form filter with a posterior distribution whose moments are derived in the paper. Finally, we evaluate the performance of both classes of functions through simulation of an unmanned ground vehicle.
  • Publication
    Approximation Metrics for Discrete and Continuous Systems
    (2005-01-01) Girard, Antoine; Pappas, George J
    Established system relationships for discrete systems, such as language inclusion, simulation, and bisimulation, require system observations to be identical. When interacting with the physical world, modeled by continuous or hybrid systems, exact relationships are restrictive and not robust. In this paper, we develop the first framework of system approximation that applies to both discrete and continuous systems by developing notions of approximate language inclusion, approximate simulation, and approximate bisimulation relations. We define a hierarchy of approximation pseudo-metrics between two systems that quantify the quality of the approximation, and capture the established exact relationships as zero sections. Our approximation framework is compositional for synchronous composition operators. Algorithms are developed for computing the proposed pseudo-metrics, both exactly and approximately. The exact algorithms require the generalization of the fixed point algorithms for computing simulation and bisimulation relations, or dually, the solution a static game whose cost is the so-called branching distance between the systems. Approximations for the pseudo-metrics can be obtained by considering Lyapunov-like functions called simulation and bisimulation functions. We illustrate our approximation framework in reducing the complexity of safety verification problems for both deterministic and nondeterministic continuous systems.
  • Publication
    Hybrid Modeling and Experimental Cooperative Control of Multiple Unmanned Aerial Vehicles
    (2004-12-10) Bayraktar, Selcuk; Fainekos, Georgios E; Pappas, George J
    Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (U.A.V.s), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-the-loop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.
  • Publication
    Automatic Verification of Linear Controller Software
    (2015-10-01) Park, Junkil; Lee, Insup; Pappas, George J; Pajic, Miroslav; Sokolsky, Oleg
    We consider the problem of verification of software implementations of linear time-invariant controllers. Commonly, different implementations use different representations of the controller’s state, for example due to optimizations in a third-party code generator. To accommodate this variation, we exploit input-output controller specification captured by the controller’s transfer function and show how to automatically verify correctness of C code controller implementations using a Frama-C/Why3/Z3 toolchain. Scalability of the approach is evaluated using randomly generated controller specifications of realistic size.
  • Publication
    Adaptive Robot Deployment Algorithms
    (2010-03-25) LE NY, Jerome; Pappas, George J
    In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, and their convergence properties analyzed via the theory of stochastic approximations. This approach yields often surprisingly simple algorithms that can accommodate complicated objective functions, and adapt to slow temporal variations in environmental parameters. To illustrate the richness of the framework, we discuss several applications, including searching for a field extrema, deployment with stochastic connectivity constraints, coverage, and vehicle routing scenarios.
  • Publication
    Modeling and Analyzing Biomolecular Networks
    (2002-01-01) Alur, Rajeev; Kumar, R. Vijay; Mintz, Max L; Pappas, George J; Belta, Calin; Rubin, Harvey; Schug, Jonathan
    The authors argue for the need to model and analyze biological networks at molecular and cellular levels. They propose a computational toolbox for biologists. Central to their approach is the paradigm of hybrid models in which discrete events are combined with continuous differential equations to capture switching behavior.
  • Publication
    Distributed Topology Control of Dynamic Networks
    (2008-06-11) Zavlanos, Michael M; Tahbaz-Salehi, Alireza; Jadbabaie, Ali; Pappas, George J
    In this paper, we present a distributed control framework for controlling the topology of dynamic multi-agent networks. Agents are equipped with local sensing and wireless communication capabilities, however, due to power constraints, they are required to switch between two modes of operation, namely active and sleep. The control objective investigated in this paper is to determine distributed coordination protocols that regulate switching between the operation modes of every agent such that the overall network guarantees multi-hop communication links among a subset of so called boundary agents. In the proposed framework, coordination is based on a virtual market where every request to switch off is associated with a bid. Combinations of requests are verified with respect to connectivity and the one corresponding to the highest aggregate bid is finally served. Other than nearest neighbor information, our approach assumes no knowledge of the network topology, while verification of connectivity relies on notions of algebraic graph theory as well as gossip algorithms run over the network. Integration of the individual controllers results in an asynchronous networked control system for which we show that it satisfies the connectivity specification almost surely. We finally illustrate efficiency of our scalable approach in nontrivial computer simulations.