426 research outputs found
Distributed Adaptive Fault-Tolerant Control of Uncertain Multi-Agent Systems
This paper presents an adaptive fault-tolerant control (FTC) scheme for a
class of nonlinear uncertain multi-agent systems. A local FTC scheme is
designed for each agent using local measurements and suitable information
exchanged between neighboring agents. Each local FTC scheme consists of a fault
diagnosis module and a reconfigurable controller module comprised of a baseline
controller and two adaptive fault-tolerant controllers activated after fault
detection and after fault isolation, respectively. Under certain assumptions,
the closed-loop system's stability and leader-follower consensus properties are
rigorously established under different modes of the FTC system, including the
time-period before possible fault detection, between fault detection and
possible isolation, and after fault isolation
An Adaptive Approach to Sensor Bias Fault Diagnosis and Accommodation for a Class of Input-Output Nonlinear Systems
This paper presents an adaptive sensor fault
diagnosis and accommodation scheme for multiple sensor bias
faults for a class of input-output nonlinear systems subject to
modeling uncertainty and measurement noise. The proposed
scheme consists of a nonlinear estimation model that includes
an adaptive component which is initiated upon the detection
of a fault, in order to approximate the magnitude of the
bias faults. A detectability condition characterizing the class of
detectable sensor bias faults is derived and the robustness and
stability properties of the adaptive scheme are presented. The
estimation of the magnitude of the sensor bias faults allows the
identification of the faulty sensors and it is also used for fault
accommodation purposes. The effectiveness of the proposed
scheme is demonstrated through a simulation example
Almost Sure Resilient Consensus Under Stochastic Interaction: Links Failure and Noisy Channels
The resilient consensus problem over a class
of discrete-time linear multiagent systems is addressed.
Because of external cyber-attacks, some agents are assumed
to be malicious and not following a desired cooperative
behavior. Thus, the objective consists in designing a
control strategy for the healthy agents to reach consensus
upon their state vectors, while due to interaction among the
agents, the malicious agents try to prevent them to achieve
consensus. Although this problem has been investigated
by some researchers, under the existing approaches in the
literature, achieving consensus is only guaranteed when
the information exchange among the agents is deterministic.
Based on this motivation, the main contribution of
the paper is on almost sure resilient consensus control of
a network of healthy agents in the presence of stochastic
links failure and communication noises. We design a
discrete-time protocol for the set of the healthy agents, and
we show that under some probabilistic conditions on interaction
among the agents, achieving almost sure consensus
among the healthy agents can be guaranteed. The results
also are verified by numerical examples
A Robust Nonlinear Observer-based Approach for Distributed Fault Detection of Input-Output Interconnected Systems
This paper develops a nonlinear observer-based approach for distributed fault detection of a class of interconnected
inputâoutput nonlinear systems, which is robust to modeling uncertainty and measurement
noise. First, a nonlinear observer design is used to generate the residual signals required for fault detection.
Then, a distributed fault detection scheme and the corresponding adaptive thresholds are designed
based on the observer characteristics and, at the same time, filtering is used in order to attenuate the effect
of measurement noise, which facilitates less conservative thresholds and enhanced robustness. Finally, a
fault detectability condition characterizing quantitatively the class of detectable faults is derived
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
In our digital universe nowadays, enormous amount of data are produced in a
streaming manner in a variety of application areas. These data are often
unlabelled. In this case, identifying infrequent events, such as anomalies,
poses a great challenge. This problem becomes even more difficult in
non-stationary environments, which can cause deterioration of the predictive
performance of a model. To address the above challenges, the paper proposes an
autoencoder-based incremental learning method with drift detection
(strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of
both incremental learning and drift detection. We conduct an experimental study
using real-world and synthetic datasets with severe or extreme class imbalance,
and provide an empirical analysis of strAEm++DD. We further conduct a
comparative study, showing that the proposed method significantly outperforms
existing baseline and advanced methods.Comment: anomaly detection, concept drift, incremental anomaly detection,
concept drift, incremental learning, autoencoders, data streams, class
imbalance, nonstationary environment
Data-efficient Online Classification with Siamese Networks and Active Learning
An ever increasing volume of data is nowadays becoming available in a
streaming manner in many application areas, such as, in critical infrastructure
systems, finance and banking, security and crime and web analytics. To meet
this new demand, predictive models need to be built online where learning
occurs on-the-fly. Online learning poses important challenges that affect the
deployment of online classification systems to real-life problems. In this
paper we investigate learning from limited labelled, nonstationary and
imbalanced data in online classification. We propose a learning method that
synergistically combines siamese neural networks and active learning. The
proposed method uses a multi-sliding window approach to store data, and
maintains separate and balanced queues for each class. Our study shows that the
proposed method is robust to data nonstationarity and imbalance, and
significantly outperforms baselines and state-of-the-art algorithms in terms of
both learning speed and performance. Importantly, it is effective even when
only 1% of the labels of the arriving instances are available.Comment: 2020 International Joint Conference on Neural Networks (IJCNN),
Glasgow, UK, 202
Distributed AdaptiveFault-Tolerant Control of Uncertain Multi-Agent Systems
This brief paper presents a distributed adaptive fault-tolerant leader-following consensus control scheme for a class of nonlinear uncertain multi-agent systems under a bidirectional communication topology with possibly asymmetric weights and subject to process and actuator faults. A local fault-tolerant control (FTC) component is designed for each agent using local measurements and suitable information exchanged between neighboring agents. Each local FTC component consists of a fault diagnosis module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault-tolerant controllers activated after fault detection and after fault isolation, respectively. By using an appropriately chosen Lyapunov function, the closed-loop stability and asymptotic convergence property of leaderâfollower consensus are rigorously established under different operating modes of the FTC system
Distributed adaptive fault-tolerant leader-following formation control of nonlinear uncertain second-order multi-agent systems
This paper presents a distributed integrated fault diagnosis and accommodation scheme for leaderâfollowing formation control of a class of nonlinear uncertain secondâorder multiâagent systems. The fault model under consideration includes both process and actuator faults, which may evolve abruptly or incipiently. The timeâvarying leader communicates with a small subset of follower agents, and each follower agent communicates to its directly connected neighbors through a bidirectional network with possibly asymmetric weights. A local fault diagnosis and accommodation component are designed for each agent in the distributed system, which consists of a fault detection and isolation module and a reconfigurable controller module comprised of a baseline controller and two adaptive faultâtolerant controllers, activated after fault detection and after fault isolation, respectively. By using appropriately the designed Lyapunov functions, the closedâloop stability and asymptotic convergence properties of the leaderâfollower formation are rigorously established under different modes of the faultâtolerant control system
Distributed Fault Diagnosis using Sensor Networks and Consensus-based Filters
This paper considers the problem of designing distributed fault diagnosis algorithms for dynamic systems using sensor networks. A network of distributed estimation agents is designed where a bank of local Kalman filters is embedded into each sensor. The diagnosis decision is performed by a distributed hypothesis testing method that relies on a belief consensus algorithm. Under certain assumptions, both the distributed estimation and the diagnosis algorithms are derived from their centralized counterparts thanks to dynamic average-consensus techniques. Simulation results are provided to demonstrate the effectiveness of the proposed architecture and algorithm
Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone
This work investigates the problem of simultaneous tracking and jamming of a
rogue drone in 3D space with a team of cooperative unmanned aerial vehicles
(UAVs). We propose a decentralized estimation, decision and control framework
in which a team of UAVs cooperate in order to a) optimally choose their
mobility control actions that result in accurate target tracking and b) select
the desired transmit power levels which cause uninterrupted radio jamming and
thus ultimately disrupt the operation of the rogue drone. The proposed decision
and control framework allows the UAVs to reconfigure themselves in 3D space
such that the cooperative simultaneous tracking and jamming (CSTJ) objective is
achieved; while at the same time ensures that the unwanted inter-UAV jamming
interference caused during CSTJ is kept below a specified critical threshold.
Finally, we formulate this problem under challenging conditions i.e., uncertain
dynamics, noisy measurements and false alarms. Extensive simulation experiments
illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS
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