323 research outputs found
Approximating Reachable Sets for Neural Network based Models in Real-Time via Optimal Control
In this paper, we present a data-driven framework for real-time estimation of
reachable sets for control systems where the plant is modeled using neural
networks (NNs). We utilize a running example of a quadrotor model that is
learned using trajectory data via NNs. The NN learned offline, can be excited
online to obtain linear approximations for reachability analysis. We use a
dynamic mode decomposition based approach to obtain linear liftings of the NN
model. The linear models thus obtained can utilize optimal control theory to
obtain polytopic approximations to the reachable sets in real-time. The
polytopic approximations can be tuned to arbitrary degrees of accuracy. The
proposed framework can be extended to other nonlinear models that utilize NNs
to estimate plant dynamics. We demonstrate the effectiveness of the proposed
framework using an illustrative simulation of quadrotor dynamics.Comment: 14 pages, 11 figures, journal paper that has been conditionally
accepte
Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements
A practical challenge which arises in the operation of sensor networks is the
presence of sensor faults, biases, or adversarial attacks, which can lead to
significant errors incurring in the localization of the agents, thereby
undermining the security and performance of the network. We consider the
problem of identifying and correcting the localization errors using inter-agent
measurements, such as the distances or bearings from one agent to another,
which can serve as a redundant source of information about the sensor network's
configuration. The problem is solved by searching for a block sparse solution
to an underdetermined system of equations, where the sparsity is introduced via
the fact that the number of localization errors is typically much lesser than
the total number of agents. Unlike the existing works, our proposed method does
not require the knowledge of the identities of the anchors, i.e., the agents
that do not have localization errors. We characterize the necessary and
sufficient conditions on the sensor network configuration under which a given
number of localization errors can be uniquely identified and corrected using
the proposed method. The applicability of our results is demonstrated
numerically by processing inter-agent distance measurements using a sequential
convex programming (SCP) algorithm to identify the localization errors in a
sensor network
Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems
The conventional solutions for fault-detection, identification, and
reconstruction (FDIR) require centralized decision-making mechanisms which are
typically combinatorial in their nature, necessitating the design of an
efficient distributed FDIR mechanism that is suitable for multi-agent
applications. To this end, we develop a general framework for efficiently
reconstructing a sparse vector being observed over a sensor network via
nonlinear measurements. The proposed framework is used to design a distributed
multi-agent FDIR algorithm based on a combination of the sequential convex
programming (SCP) and the alternating direction method of multipliers (ADMM)
optimization approaches. The proposed distributed FDIR algorithm can process a
variety of inter-agent measurements (including distances, bearings, relative
velocities, and subtended angles between agents) to identify the faulty agents
and recover their true states. The effectiveness of the proposed distributed
multi-agent FDIR approach is demonstrated by considering a numerical example in
which the inter-agent distances are used to identify the faulty agents in a
multi-agent configuration, as well as reconstruct their error vectors
MASTAQ: A Middleware Architecture for Sensor Applications with Statistical Quality Constraints
We present the design goals and functional components of MASTAQ, a data management middleware for pervasive applications that utilize sensor data. MASTAQ allows applications to specify their quality-of information (QoI) preferences (in terms of statistical metrics over the data) independent of the underlying network topology. It then achieves energy efficiency by adaptively activating and querying only the subset of sensor nodes needed to meet the target QoI bounds. We also present a closed-loop feedback mechanism based on broadcasting of activation probabilities, which allows MASTAQ to activate the appropriate number of sensors without requiring any inter-sensor coordination or knowledge of the actual deployment.1
Correct-by-Construction Control Design for Mixed-Invariant Systems in Lie Groups
In this paper, we use the derivative of the exponential map to derive the
exact evolution of the logarithm of the tracking error for mixed-invariant
systems. Following correct-by-construction software paradigm, we propose an
invariant control law for mixed-invariant systems, with application to Unmanned
Aerial Systems (UASs), that is designed for efficient safety verification. We
derive the nonlinear distortion matrix in the transformed differential equation
in the Lie algebra and express the distortion matrix in a series form for any
matrix Lie group and in a closed-form for the SE(2) Lie group. Given the input
distortion, we employ dynamic inversion to linearize the evolution of error
dynamics and apply a linear control strategy. We employ Linear Matrix
Inequalities (LMIs) to bound the tracking error given a bounded disturbance
amplified by the distortion matrix and leverage the tracking error bound to
create flow pipes for the creation of a Polyhedral Invariant Hybrid Automaton
(PIHA) model. We demonstrate the usefulness of our method by applying it to a
simplified holonomic aircraft and nonholonomic rover with polynomial-based path
planning methods.Comment: 15 pages, 19 figures. Submitted to IEEE TA
Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications
Research in developing data-driven models for Air Traffic Management (ATM)
has gained a tremendous interest in recent years. However, data-driven models
are known to have long training time and require large datasets to achieve good
performance. To address the two issues, this paper proposes a Multi-Agent
Bidirectional Encoder Representations from Transformers (MA-BERT) model that
fully considers the multi-agent characteristic of the ATM system and learns air
traffic controllers' decisions, and a pre-training and fine-tuning transfer
learning framework. By pre-training the MA-BERT on a large dataset from a major
airport and then fine-tuning it to other airports and specific air traffic
applications, a large amount of the total training time can be saved. In
addition, for newly adopted procedures and constructed airports where no
historical data is available, this paper shows that the pre-trained MA-BERT can
achieve high performance by updating regularly with little data. The proposed
transfer learning framework and MA-BERT are tested with the automatic dependent
surveillance-broadcast data recorded in 3 airports in South Korea in 2019.Comment: 12 pages, 8 figures, submitted for IEEE Transactions on Intelligent
Transportation Syste
CoMon: Cooperative Ambience Monitoring Platform with Continuity and Benefit Awareness
Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications
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