This document presents a thesis on leader-following consensus control of multi-agent systems with communication constraints. It introduces multi-agent systems and applications. It then discusses the motivation, leader-follower consensus model, system dynamics modeling, and Lyapunov-based consensus control design for systems with time delays and packet loss. Simulation results examine the effect of packet loss rates, time delays, and number of agents on consensus time. Experimental validation is also presented using mobile robots with wireless communication.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICL...IJITCA Journal
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model . Our proposed approach controls
deformation of target's model. If deformation of target's model is larger than a predetermined threshold,then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF) . DDPF
approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target' s model. However, DDPF approach updates target's model when the rotation or
scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficiently
and accurately.
This document describes a study on enhancing the serial estimation of discrete choice models through the use of standardization, warm starting, and early stopping techniques. It first reviews literature on accelerating discrete choice model estimation and quasi-Newton optimization methods. It then details the three techniques used: standardizing variables, initializing subsequent models with previous solutions, and stopping optimization early based on log-likelihood trends. Two sequences of 100 discrete choice models are tested to evaluate the effectiveness of the techniques. Results show that warm starting parameters and the Hessian matrix from previous solutions significantly reduces estimation time compared to estimating models separately.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
F EATURE S ELECTION USING F ISHER ’ S R ATIO T ECHNIQUE FOR A UTOMATIC ...IJCI JOURNAL
Automatic Speech Recognition (ASR) involves mainly
two steps; feature extraction and classification
(pattern recognition). Mel Frequency Cepstral Coeff
icient (MFCC) is used as one of the prominent featu
re
extraction techniques in ASR. Usually, the set of a
ll 12 MFCC coefficients is used as the feature vect
or in
the classification step. But the question is whethe
r the same or improved classification accuracy can
be
achieved by using a subset of 12 MFCC as feature ve
ctor. In this paper, Fisher’s ratio technique is us
ed for
selecting a subset of 12 MFCC coefficients that con
tribute more in discriminating a pattern. The selec
ted
coefficients are used in classification with Hidden
Markov Model (HMM) algorithm. The classification
accuracies that we get by using 12 coefficients and
by using the selected coefficients are compare
This document presents three techniques to accelerate the estimation of a sequence of discrete choice models (DCMs): standardization, warm start, and early stopping. Standardization rescales independent variables to a common scale. Warm start initializes subsequent models with the optimized parameters from previous models. Early stopping monitors the log likelihood improvement over iterations and stops estimation early if improvement plateaus. These techniques are tested on two sequences of 100 DCMs estimated using the BFGS algorithm. Results show that each technique individually and combined provide significant speedups for estimating sequences of DCMs.
A Network Selection Scheme with Adaptive Criteria Weights for 5G Vehicular Sy...University of Piraeus
Fifth Generation Vehicular Cloud Computing (5G-VCC) systems use heterogeneous network access technologies to fulfill the requirements of modern vehicular services. Efficient network selection algorithms are required to satisfy the constraints of Driver Assistance (DA) services, Passengers Entertainment and Information (PEnI) services and Medical (MED) services that provided to vehicular users. The presence of MED services affects the importance of other services in situations where patients with immediate health status exist within the vehicle. This paper proposes a network selection scheme which considers the patient health status to adapt the importance of each service. The scheme consists of two Fuzzy Multi Attribute Decision Making (FMADM) algorithms: the Trapezoidal Fuzzy Adaptive Analytic Network Process (TF-AANP) to calculate the relative importance of each vehicular service and the selection criteria, as well as the Trapezoidal Fuzzy Topsis with Adaptive Criteria Weights (TFT-ACW) to accomplish the ranking of the candidate networks. Both algorithms use Interval-Valued Trapezoidal Fuzzy Numbers (IVTFN). Performance evaluation shows that the suggested method outperforms existing algorithms by satisfying the constraints of MED services when the patient health status becomes immediate.
EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE...Zac Darcy
This paper presents a new method for evaluating the symmetric information gap between two dynamical systems using particle filters. It first describes a symmetric version of the information gap metric based on symmetric Kullback-Leibler divergence. A numerical method is then developed to approximate this symmetric K-L rate using particle filters. This represents the posterior densities of the dynamical systems as mixtures of Gaussians. The method is demonstrated on a nonlinear target tracking example, computing the symmetric information gap between two systems at each time step.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICL...IJITCA Journal
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model . Our proposed approach controls
deformation of target's model. If deformation of target's model is larger than a predetermined threshold,then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF) . DDPF
approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target' s model. However, DDPF approach updates target's model when the rotation or
scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficiently
and accurately.
This document describes a study on enhancing the serial estimation of discrete choice models through the use of standardization, warm starting, and early stopping techniques. It first reviews literature on accelerating discrete choice model estimation and quasi-Newton optimization methods. It then details the three techniques used: standardizing variables, initializing subsequent models with previous solutions, and stopping optimization early based on log-likelihood trends. Two sequences of 100 discrete choice models are tested to evaluate the effectiveness of the techniques. Results show that warm starting parameters and the Hessian matrix from previous solutions significantly reduces estimation time compared to estimating models separately.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
F EATURE S ELECTION USING F ISHER ’ S R ATIO T ECHNIQUE FOR A UTOMATIC ...IJCI JOURNAL
Automatic Speech Recognition (ASR) involves mainly
two steps; feature extraction and classification
(pattern recognition). Mel Frequency Cepstral Coeff
icient (MFCC) is used as one of the prominent featu
re
extraction techniques in ASR. Usually, the set of a
ll 12 MFCC coefficients is used as the feature vect
or in
the classification step. But the question is whethe
r the same or improved classification accuracy can
be
achieved by using a subset of 12 MFCC as feature ve
ctor. In this paper, Fisher’s ratio technique is us
ed for
selecting a subset of 12 MFCC coefficients that con
tribute more in discriminating a pattern. The selec
ted
coefficients are used in classification with Hidden
Markov Model (HMM) algorithm. The classification
accuracies that we get by using 12 coefficients and
by using the selected coefficients are compare
This document presents three techniques to accelerate the estimation of a sequence of discrete choice models (DCMs): standardization, warm start, and early stopping. Standardization rescales independent variables to a common scale. Warm start initializes subsequent models with the optimized parameters from previous models. Early stopping monitors the log likelihood improvement over iterations and stops estimation early if improvement plateaus. These techniques are tested on two sequences of 100 DCMs estimated using the BFGS algorithm. Results show that each technique individually and combined provide significant speedups for estimating sequences of DCMs.
A Network Selection Scheme with Adaptive Criteria Weights for 5G Vehicular Sy...University of Piraeus
Fifth Generation Vehicular Cloud Computing (5G-VCC) systems use heterogeneous network access technologies to fulfill the requirements of modern vehicular services. Efficient network selection algorithms are required to satisfy the constraints of Driver Assistance (DA) services, Passengers Entertainment and Information (PEnI) services and Medical (MED) services that provided to vehicular users. The presence of MED services affects the importance of other services in situations where patients with immediate health status exist within the vehicle. This paper proposes a network selection scheme which considers the patient health status to adapt the importance of each service. The scheme consists of two Fuzzy Multi Attribute Decision Making (FMADM) algorithms: the Trapezoidal Fuzzy Adaptive Analytic Network Process (TF-AANP) to calculate the relative importance of each vehicular service and the selection criteria, as well as the Trapezoidal Fuzzy Topsis with Adaptive Criteria Weights (TFT-ACW) to accomplish the ranking of the candidate networks. Both algorithms use Interval-Valued Trapezoidal Fuzzy Numbers (IVTFN). Performance evaluation shows that the suggested method outperforms existing algorithms by satisfying the constraints of MED services when the patient health status becomes immediate.
EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE...Zac Darcy
This paper presents a new method for evaluating the symmetric information gap between two dynamical systems using particle filters. It first describes a symmetric version of the information gap metric based on symmetric Kullback-Leibler divergence. A numerical method is then developed to approximate this symmetric K-L rate using particle filters. This represents the posterior densities of the dynamical systems as mixtures of Gaussians. The method is demonstrated on a nonlinear target tracking example, computing the symmetric information gap between two systems at each time step.
A NEW TOOL FOR LARGE SCALE POWER SYSTEM TRANSIENT SECURITY ASSESSMENTPower System Operation
This document proposes a new method for fast assessment of transient security power limits in large power systems using neural networks. It establishes a nonlinear mapping between transient energy margin and generator power at different fault clearing times and load levels using a self-organizing map. The transient security power limits of generators can then be estimated very quickly by inputting fault clearing time and load level data. Testing on a sample power system shows the proposed method can accurately estimate security limits without needing to calculate analytical sensitivities, providing faster results than traditional methods.
Traffic light control in non stationary environments based on multiMohamed Omari
This three sentence summary provides the key details about the document:
The document proposes using multi-agent Q-learning to control traffic lights in a large, non-stationary traffic network. Each intersection is modeled as an intelligent agent that uses reinforcement learning to determine optimal light timings based on local queue lengths. The Q-learning approach does not require a pre-specified model and can adapt to changing traffic conditions, making it suitable for dynamic, non-stationary environments unlike traditional reinforcement learning.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Online learning in estimation of distribution algorithms for dynamic environm...André Gonçalves
This document proposes a new estimation of distribution algorithm called EDAOGMM that uses an online Gaussian mixture model to optimize problems in dynamic environments. EDAOGMM adapts its internal model through online learning as the environment changes. It was tested on benchmark dynamic optimization problems and outperformed other state-of-the-art algorithms, especially in high-frequency changing environments. Future work includes improving EDAOGMM's ability to avoid premature convergence and further experimental testing.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
Dimensionality Reduction Evolution and Validationiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
1. The document discusses using Markov chain models to simulate channel allocation in cognitive radio networks. It involves modeling the switching between channel states as a continuous-time Markov process.
2. The methodology involves choosing random subsets of the cognitive radio network that can select between two events. This forms a network with four possible states. A probability transition matrix is computed to model state transitions.
3. Related work on using hidden Markov models for spectrum sensing in cognitive radio networks is reviewed. Most existing approaches model primary user channel occupancy as a discrete-time Markov chain, but some argue more advanced models may be needed to accurately capture statistics.
2013 feature selection for intrusion detection using nsl kddVan Thanh
This document proposes a new feature selection method for intrusion detection and compares it to existing methods using the NSL-KDD dataset. It begins by introducing the need for efficient feature selection due to increasing network traffic volumes. It then discusses existing feature selection methods and the NSL-KDD dataset. The document proposes a new feature selection method called Attribute Ratio that calculates ratios based on attribute averages and frequencies for each class. An experimental study is described that applies this method and existing methods to the NSL-KDD dataset and evaluates them using a decision tree classifier.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
A scalable collaborative filtering framework based on co-clusteringlau
This document summarizes a research paper that proposes a scalable collaborative filtering framework based on co-clustering. It introduces a dynamic collaborative filtering approach that supports new users, items, and ratings using incremental and batch versions of the co-clustering algorithm. Experimental results on a movie rating dataset show the co-clustering approach provides comparable prediction accuracy to SVD, NNMF, and correlation-based methods but with much lower computational effort.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a data driven prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).
This document presents an approach for improving maintenance policies for multi-state systems. It first formalizes the transition process of a multi-state system using dynamic Bayesian networks. It then exhibits a cost function for preventive maintenance and an optimization method using reinforcement learning to identify the best combination of transition rates and preventive maintenance policy. The dynamic Bayesian network approach models the probability distributions of the system's state over time and allows for more compact representation compared to Markov chains. The reinforcement learning optimization seeks to minimize cost and maximize availability by learning the optimal preventive maintenance levels over the system's lifetime.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Cellular wireless systems like GSM suffer from congestion resulting in overall system degradation and poor service delivery. When the traffic demand in a geographical area is high, the input traffic rate will exceed thecapacity of the output lines. This work focused on homogenous wireless network (the network traffic and resource dimensioning that are statistically identical) such that the network performance
evaluation can be reduced to a system with single cell and a single traffic type. Such system can employa queuing model to evaluate the performance metric of a cell in terms of blocking probability.
Five congestion control models were compared in the work to ascertain their peculiarities, they are Erlang B, Erlang C, Engset (cleared), Engset (buffered), and Bernoulli. To analyze the system, an aggregate onedimensional Markov chain wasderived, such that it describes a call arrival process under the assumption
that it is Poisson distributed. The models were simulated and their results show varying performances, however the Bernoulli model (Pb5) tends to show a situation that allows more users access to the system and the congestion level remain unaffected despite increase in the number of users and the offered traffic into the system.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
The document discusses approaches to modeling and controlling traffic, including macroscopic and microscopic models. It then describes the components of a typical traffic light control system and various control algorithms that have been used, including expert systems, evolutionary algorithms, fuzzy logic, and reinforcement learning. The overall goals are to improve safety, minimize travel time, and increase infrastructure capacity.
Addressing the Multichannel Selection, Scheduling and Coordination Costpijans
The document discusses a new challenge called the Multichannel Scheduling Cost (MSC) that arises in multichannel networks due to the inability of the control channel to schedule simultaneous transmissions, resulting in data channels lying idle. To address this, a Cyclic Scheduling Algorithm (CSA) is proposed that employs one control channel and 4 data channels using a proactive channel reservation scheme. The scheme reserves data channels while they are still busy transmitting to reduce idle times. Simulation results show the CSA scheme improves bandwidth utilization by limiting the effects of the MSC to the first scheduling cycle.
A DDRESSING T HE M ULTICHANNEL S ELECTION , S CHEDULING A ND C OORDINATION...pijans
We discuss a new multichannel coordination and sche
duling challenge called the Multichannel Scheduling
Cost (MSC). It is caused by the inability of the co
ntrol channel to schedule simultaneous transmission
s
resulting in data channels lying idle and their ban
dwidth underutilized. As a result, wasted bandwidth
increases as the number of data channels increases.
To address this challenge, a cyclic scheduling
Algorithm (CSA) is proposed. It employs one dedicat
ed signaling channel and 4 data channels. It is
premised on a proactive channel reservation scheme
which reduces the idleness of channels. The scheme
ensures that data channels are reserved while they
are still busy. The data channels are reserved whil
e
their remaining transmission duration is equal to t
he virtual carrier sensing duration. This gives the
next
pair sufficient time to reserve the current data ch
annel before it becomes available, limiting the MSC
to the
first cycle. The simulation results show a performa
nce benefit of the CSA scheme in addressing the eff
ects
of the MSC
A Simulation Experiment on a Built-In Self Test Equipped with Pseudorandom Te...VLSICS Design
This paper investigates the impact of the changes of the characteristic polynomials and initial loadings, on behaviour of aliasing errors of parallel signature analyzer (Multi-Input Shift Register), used in an LFSR based digital circuit testing technique. The investigation is carried-out through an extensive simulation study of the effectiveness of the LFSR based digital circuit testing technique. The results of the study show that when the identical characteristic polynomials of order n are used in both pseudo-random test-pattern generator, as well as in Multi-Input Shift Register (MISR) signature analyzer (parallel type) then the probability of aliasing errors remains unchanged due to the changes in the initial loadings of the pseudo-random test-pattern generator.
A Simulation Experiment on a Built-In Self Test Equipped with Pseudorandom Te...VLSICS Design
This paper investigates the impact of the changes of the characteristic polynomials and initial loadings, on behaviour of aliasing errors of parallel signature analyzer (Multi-Input Shift Register), used in an LFSR based digital circuit testing technique. The investigation is carried-out through an extensive simulation study of the effectiveness of the LFSR based digital circuit testing technique. The results of the study show that when the identical characteristic polynomials of order n are used in both pseudo-random test-pattern generator, as well as in Multi-Input Shift Register (MISR) signature analyzer (parallel type) then the probability of aliasing errors remains unchanged due to the changes in the initial loadings of the pseudo-random test-pattern generator.
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
A NEW TOOL FOR LARGE SCALE POWER SYSTEM TRANSIENT SECURITY ASSESSMENTPower System Operation
This document proposes a new method for fast assessment of transient security power limits in large power systems using neural networks. It establishes a nonlinear mapping between transient energy margin and generator power at different fault clearing times and load levels using a self-organizing map. The transient security power limits of generators can then be estimated very quickly by inputting fault clearing time and load level data. Testing on a sample power system shows the proposed method can accurately estimate security limits without needing to calculate analytical sensitivities, providing faster results than traditional methods.
Traffic light control in non stationary environments based on multiMohamed Omari
This three sentence summary provides the key details about the document:
The document proposes using multi-agent Q-learning to control traffic lights in a large, non-stationary traffic network. Each intersection is modeled as an intelligent agent that uses reinforcement learning to determine optimal light timings based on local queue lengths. The Q-learning approach does not require a pre-specified model and can adapt to changing traffic conditions, making it suitable for dynamic, non-stationary environments unlike traditional reinforcement learning.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Online learning in estimation of distribution algorithms for dynamic environm...André Gonçalves
This document proposes a new estimation of distribution algorithm called EDAOGMM that uses an online Gaussian mixture model to optimize problems in dynamic environments. EDAOGMM adapts its internal model through online learning as the environment changes. It was tested on benchmark dynamic optimization problems and outperformed other state-of-the-art algorithms, especially in high-frequency changing environments. Future work includes improving EDAOGMM's ability to avoid premature convergence and further experimental testing.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
Dimensionality Reduction Evolution and Validationiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
1. The document discusses using Markov chain models to simulate channel allocation in cognitive radio networks. It involves modeling the switching between channel states as a continuous-time Markov process.
2. The methodology involves choosing random subsets of the cognitive radio network that can select between two events. This forms a network with four possible states. A probability transition matrix is computed to model state transitions.
3. Related work on using hidden Markov models for spectrum sensing in cognitive radio networks is reviewed. Most existing approaches model primary user channel occupancy as a discrete-time Markov chain, but some argue more advanced models may be needed to accurately capture statistics.
2013 feature selection for intrusion detection using nsl kddVan Thanh
This document proposes a new feature selection method for intrusion detection and compares it to existing methods using the NSL-KDD dataset. It begins by introducing the need for efficient feature selection due to increasing network traffic volumes. It then discusses existing feature selection methods and the NSL-KDD dataset. The document proposes a new feature selection method called Attribute Ratio that calculates ratios based on attribute averages and frequencies for each class. An experimental study is described that applies this method and existing methods to the NSL-KDD dataset and evaluates them using a decision tree classifier.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
A scalable collaborative filtering framework based on co-clusteringlau
This document summarizes a research paper that proposes a scalable collaborative filtering framework based on co-clustering. It introduces a dynamic collaborative filtering approach that supports new users, items, and ratings using incremental and batch versions of the co-clustering algorithm. Experimental results on a movie rating dataset show the co-clustering approach provides comparable prediction accuracy to SVD, NNMF, and correlation-based methods but with much lower computational effort.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a data driven prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).
This document presents an approach for improving maintenance policies for multi-state systems. It first formalizes the transition process of a multi-state system using dynamic Bayesian networks. It then exhibits a cost function for preventive maintenance and an optimization method using reinforcement learning to identify the best combination of transition rates and preventive maintenance policy. The dynamic Bayesian network approach models the probability distributions of the system's state over time and allows for more compact representation compared to Markov chains. The reinforcement learning optimization seeks to minimize cost and maximize availability by learning the optimal preventive maintenance levels over the system's lifetime.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Cellular wireless systems like GSM suffer from congestion resulting in overall system degradation and poor service delivery. When the traffic demand in a geographical area is high, the input traffic rate will exceed thecapacity of the output lines. This work focused on homogenous wireless network (the network traffic and resource dimensioning that are statistically identical) such that the network performance
evaluation can be reduced to a system with single cell and a single traffic type. Such system can employa queuing model to evaluate the performance metric of a cell in terms of blocking probability.
Five congestion control models were compared in the work to ascertain their peculiarities, they are Erlang B, Erlang C, Engset (cleared), Engset (buffered), and Bernoulli. To analyze the system, an aggregate onedimensional Markov chain wasderived, such that it describes a call arrival process under the assumption
that it is Poisson distributed. The models were simulated and their results show varying performances, however the Bernoulli model (Pb5) tends to show a situation that allows more users access to the system and the congestion level remain unaffected despite increase in the number of users and the offered traffic into the system.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
The document discusses approaches to modeling and controlling traffic, including macroscopic and microscopic models. It then describes the components of a typical traffic light control system and various control algorithms that have been used, including expert systems, evolutionary algorithms, fuzzy logic, and reinforcement learning. The overall goals are to improve safety, minimize travel time, and increase infrastructure capacity.
Addressing the Multichannel Selection, Scheduling and Coordination Costpijans
The document discusses a new challenge called the Multichannel Scheduling Cost (MSC) that arises in multichannel networks due to the inability of the control channel to schedule simultaneous transmissions, resulting in data channels lying idle. To address this, a Cyclic Scheduling Algorithm (CSA) is proposed that employs one control channel and 4 data channels using a proactive channel reservation scheme. The scheme reserves data channels while they are still busy transmitting to reduce idle times. Simulation results show the CSA scheme improves bandwidth utilization by limiting the effects of the MSC to the first scheduling cycle.
A DDRESSING T HE M ULTICHANNEL S ELECTION , S CHEDULING A ND C OORDINATION...pijans
We discuss a new multichannel coordination and sche
duling challenge called the Multichannel Scheduling
Cost (MSC). It is caused by the inability of the co
ntrol channel to schedule simultaneous transmission
s
resulting in data channels lying idle and their ban
dwidth underutilized. As a result, wasted bandwidth
increases as the number of data channels increases.
To address this challenge, a cyclic scheduling
Algorithm (CSA) is proposed. It employs one dedicat
ed signaling channel and 4 data channels. It is
premised on a proactive channel reservation scheme
which reduces the idleness of channels. The scheme
ensures that data channels are reserved while they
are still busy. The data channels are reserved whil
e
their remaining transmission duration is equal to t
he virtual carrier sensing duration. This gives the
next
pair sufficient time to reserve the current data ch
annel before it becomes available, limiting the MSC
to the
first cycle. The simulation results show a performa
nce benefit of the CSA scheme in addressing the eff
ects
of the MSC
A Simulation Experiment on a Built-In Self Test Equipped with Pseudorandom Te...VLSICS Design
This paper investigates the impact of the changes of the characteristic polynomials and initial loadings, on behaviour of aliasing errors of parallel signature analyzer (Multi-Input Shift Register), used in an LFSR based digital circuit testing technique. The investigation is carried-out through an extensive simulation study of the effectiveness of the LFSR based digital circuit testing technique. The results of the study show that when the identical characteristic polynomials of order n are used in both pseudo-random test-pattern generator, as well as in Multi-Input Shift Register (MISR) signature analyzer (parallel type) then the probability of aliasing errors remains unchanged due to the changes in the initial loadings of the pseudo-random test-pattern generator.
A Simulation Experiment on a Built-In Self Test Equipped with Pseudorandom Te...VLSICS Design
This paper investigates the impact of the changes of the characteristic polynomials and initial loadings, on behaviour of aliasing errors of parallel signature analyzer (Multi-Input Shift Register), used in an LFSR based digital circuit testing technique. The investigation is carried-out through an extensive simulation study of the effectiveness of the LFSR based digital circuit testing technique. The results of the study show that when the identical characteristic polynomials of order n are used in both pseudo-random test-pattern generator, as well as in Multi-Input Shift Register (MISR) signature analyzer (parallel type) then the probability of aliasing errors remains unchanged due to the changes in the initial loadings of the pseudo-random test-pattern generator.
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
COMPARATIVE ANALYSIS OF CONVENTIONAL PID CONTROLLER AND FUZZY CONTROLLER WIT...IJITCA Journal
All the real systems exhibits non-linear nature,conventional controllers are not always able to provide good and accurate results. Fuzzy Logic Control is used to obtain better response. A model for simulation is designed and all the assumptions are made before the development of the model. An attempt has been made to analyze the efficiency of a fuzzy controller over a conventional PID controller for a three tank level control system using fuzzification & defuzzification methods and their responses are compared. Analysis is done through computer simulation using Matlab/Simulink toolbox. This study shows that the application of Fuzzy Logic Controller (FLC) gives the best response with triangular membership function and centroid defuzzification method.
Data-Driven Security Assessment of Power Grids Based on Machine Learning Appr...Power System Operation
Security assessment is a fundamental function for both short-term and long-term power system operations. The data-driven security assessment (DSA) can provide system stability margin without the need for detailed dynamic simulation. DSA is very helpful for control room applications such as online security assessment and day ahead or real-time dispatch scheduling with regard to system security constraints.
This paper investigates a data-driven security assessment of electric power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm because of its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool: transient stability, frequency stability, and small-signal stability. The estimation values from the machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the aforementioned three stabilities.
C:\Documents And Settings\User\桌面\Sciece Talk投影片\Science Talk 100111 陸行guestf4730f1
The document discusses computing waiting times for queueing systems using mathematical models. It introduces concepts such as average waiting time, variance of waiting time, probability distribution functions of waiting time, and models like M/M/1 queues. It provides examples of applying these concepts and models to analyze single line and multiple line queues, and compares the performance of single vs multiple server systems.
The document discusses computing waiting times for queueing systems using queueing theory. It introduces concepts such as average waiting time, variance of waiting time, probability distribution functions of waiting time, M/M/1 queue models, and performance measures like throughput and utilization. Examples are provided to illustrate comparisons between single and multiple server queue models.
Critical clearing time estimation of multi-machine power system transient st...IJECEIAES
This document presents a fuzzy logic method for estimating the critical clearing time (CCT) of faults in multi-machine power systems. The method involves a two-step fuzzy logic algorithm: 1) Calculate the time increment (Δt) based on maximum angle deviation (δmax) as input, 2) Classify system stability based on mechanical input power (Pm) and average acceleration (Aav) as inputs. The fuzzy logic method is tested on IEEE 9-bus and 39-bus systems and shows accuracy within 5% of time domain simulation results, while reducing computation time by about half. Tables compare estimated CCT and computation time between the two methods for different fault locations.
Congestion Control in Wireless Sensor Networks Using Genetic AlgorithmEditor IJCATR
Sensor network consists of a large number of small nods, strongly interacting with the physical environment, takes
environmental data through sensors, and reacts after processing on information. Wireless network technologies are widely used in most
applications. As wireless sensor networks have many activities in the field of information transmission, network congestion cannot be
thus avoided. So it seems necessary that some new methods can control congestion and use existing resources for providing better traffic
demands. Congestion increases packet loss and retransmission of removed packets and also wastes of energy. In this paper, a novel
method is presented for congestion control in wireless sensor networks using genetic algorithm. The results of simulation show that the
proposed method, in comparison with the algorithm LEACH, can significantly improve congestion control at high speeds.
Compensation of Data-Loss in Attitude Control of Spacecraft Systems rinzindorjej
In this paper, a comprehensive comparison of two robust estimation techniques namely, compensated closed-loop Kalman filtering and open-loop Kalman filtering is presented. A common problem of data loss in a real-time control system is investigated through these two schemes. The open-loop scheme, dealing with the data-loss, suffers from several shortcomings. These shortcomings are overcome using compensated scheme, where an accommodating observation signal is obtained through linear prediction technique -- a closed-loop setting and is adopted at a posteriori update step. The calculation and employment of accommodating observation signal causes computational complexity. For simulation purpose, a linear time invariant spacecraft model is however, obtained from the nonlinear spacecraft attitude dynamics through linearization at nonzero equilibrium points -- achieved off-line through Levenberg-Marguardt iterative scheme. Attempt has been made to analyze the selected example from most of the perspectives in order to display the performance of the two techniques.
New Proposed Contention Avoidance Scheme for Distributed Real-Time Systemspaperpublications3
Abstract: One method to handle collisions in a contention based distributed system is to optimize collision detection and subsequent recovery. An alternative method to handle collisions in a contention based system is to attempt to avoid them. Some systems may utilize a strict scheduling guideline to identify who may use which resources when. Other systems may have the senders listen to the channel immediately prior to transmitting and determine suitable times to transmit. A primary challenge in Distributed Real-Time Systems applications is how to carry out data given source-to-sink, end-to-end deadlines when the communication resources are scarce. A new scheme resolves collisions and tries to reduce the number of potential collision events. In this paper, we develop New Avoiding Contention Scheme that delays data packet transmission nonlinearly during forwarding for a duration that correlates with their remaining deadline and distance to the destination, and avoiding the contention in bursty traffic by using multi-path routing.
Similar to Defence Presentation [Autosaved] Final (20)
New Proposed Contention Avoidance Scheme for Distributed Real-Time Systems
Defence Presentation [Autosaved] Final
1. Examining Committee:
Dr. Ya-Jun Pan (Supervisor)
Dr. Jason Gu (External)
Dr. Robert Bauer (Internal)
Moderator: Dr. George Jarjoura
- Presented by Ajinkya Pawar (M.A.Sc Candidate)
Leader-following Consensus
of Multi-agent System with
Communication Constraints
using Lyapunov-based
Control
2. Presentation Outline
Introduction to Multi-agent Systems (MAS)
Leader-following Consensus of MAS
Consensus Control of MAS
Simulink Results
Experimental Setup and Results
Conclusion and Future Works
2
3. Multi-agents Systems (MAS)
An Agent is defined as a computational entity
that can sense and act as well as decide on its
actions in accordance with some assigned
tasks or goals.
Multi-agent System (MAS) is a specific type of
system which composes of several agents that
interact with each other to achieve certain
objectives.
Agent
Environment
Sensory
Input
Action
Output
Source: http://www.dcsc.tudelft.nl/Research
3
4. Advantages of Multi-agents Systems (MAS)
Distributes computational resources and capabilities.
MAS models problems in terms of autonomous interacting agents.
MAS efficiently retrieves and filters the global information states.
Can work and also find solutions in conditions where it is difficult for human
to reach or even to work.
Comparing with independent working of agents, MAS seems more reliable
and more efficient.
Decentralized MAS eradicates the system failure chances. 4
5. Applications of Multi-agents Systems (MAS)
Formation Control
Autonomous Formation Flight (AFF).
Unmanned Aerial Vehicles (UAVs)
significantly attracted military’s interest
because of low cost, easy maneuver, high
stability and zero casualty.
AFF control laws utilize a combination of local
and global information states.
https://ocw.mit.edu/courses/aeronautics-
and-astronautics/16-886
NASA, in 2002,
implemented AFF by
using F/A-18 fighters.
5
6. Applications of Multi-agents Systems (MAS)
Rendezvous and Cooperative Surveillance
Rendezvous problem involves bringing a
collection of vehicles to a common location at a
common time.
Cooperative Surveillance involves using several
vehicles to maintain a centralized or
decentralized description of the state of a
geographical area.
http://users.cms.caltech.edu/~murray/p
reprints/mur07
Information states about
spatially fixed or
moving entities are part
of surveillance. 6
7. Applications of Multi-agents Systems (MAS)
Environmental Sampling
Autonomous Ocean Sampling Network (AOSN)
consists of robotic vehicles that are used for
“adaptive sampling”.
The vehicles traverse random paths to record
observations. This approach allows the sensors to
be positioned in areas where they are highly
efficient.
http://users.cms.caltech.edu/~mu
rray/preprints/mur07
Cooperative control
strategy is used to
control motion of
vehicles. 7
8. Applications of Multi-agents Systems (MAS)
Intelligent Transport System
Make use of modern communication and
information technology to increase the efficiency
of transport management system in order to
optimize vehicle life, fuel efficiency, safety and
traffic.
California Partners for Advanced Transit and
Highways (PATH) demonstrated automatic
highway system.
http://www.horiba-mira.com/MIRA/
Also suitable for air
traffic control.
8
9. Thesis Motivation
9
Wireless networked communication control systems pose different
challenges to control engineers like time-delays, packet data loss,
switching topologies, noise, quantization error etc.
Very few literature on the leader-following consensus of MAS with
presence of both time delays as well as packet dropout.
Lyapunov-based control methodology to be adopted for consensus
10. Leader-follower Consensus of MAS
Leader
Follower 3Follower 1 Follower 2
Source: http://users.ece.gatech.edu/
Agents are differentiated as leaders and followers.
Leader agent follows pre-assigned trajectory or
generates it’s own trajectory.
Follower agent tracks the leader’s trajectory.
Follower agent tries to reduce its distance from the
leader agent.
Leader-following consensus can be easily
extended to leader-formation control.
10
26. Simulink Results
26
There are five conditions for which Simulink results are plotted but categorized
in three cases.
Case 1: Effect of different data loss rate without time delays on consensus of
MAS.
Case 2: Effect of time delays on consensus of MAS, where one condition is
with constant time-delay and other condition with time-varying delay.
Case 3: Effect of increasing the number of agents on consensus of MAS,
where one condition is without time-delay and other condition with constant
time delay.
27. Simulink Results (Case 1)
27
Case 1: Effect of different data loss rate without time delays on consensus
of MAS.
The directed graph topology for this case is,
The adjacency and Laplacian matrix are:
28. Simulink Results (Case 1)
28
Example 1: Data Loss Rate, r = 0%
i.e. no data loss rate (ideal condition)
Example 2: Data Loss Rate, r = 10%
29. Simulink Results (Case 1)
29
Example 3: Data Loss Rate, r = 20% Example 4: Data Loss Rate, r = 30%
30. Simulink Results (Case 1)
30
Example 5: Data Loss Rate, r = 80% Example 6: Data Loss Rate, r = 98%
31. Simulink Results (Case 1)
31
Summary of the effect of increase in data loss rate on consensus time
Example
Data Loss Rate in
%
Consensus Time
(seconds)
1 0 19
2 10 27
3 20 36
4 30 102
5 80 300
6 98 1200 or inf
32. Simulink Results (Case 2)
32
Case 2: Effect of time delays on consensus of MAS
The directed graph topology for this case is,
The adjacency and Laplacian matrix are:
33. Simulink Results (Case 2 – Condition 1)
33
Condition 1: Effect of constant time delay and fixed data loss rate at 10%
Example 1: Data Loss Rate, r = 10%
and Constant time-delay = 0.001
seconds or 1 millisecond
Example 2: Data Loss Rate, r = 10%
and Constant time-delay = 0.005
seconds or 5 milliseconds
34. Simulink Results (Case 2 – Condition 1)
34
Condition 1: Effect of constant time delay and fixed data loss rate at 10%
Example 3: Data Loss Rate, r = 10%
and Constant time-delay = 0.01
seconds or 10 milliseconds
Example 4: Data Loss Rate, r = 10%
and Constant time-delay = 0.1
seconds or 100 milliseconds
35. Simulink Results (Case 2 – Condition 1)
35
Summary of the effect of increase in constant time-delay at fixed data
loss rate of 10% on consensus time
Example
Time Delay
(milliseconds)
Consensus Time
(seconds)
1 1 33
2 5 34.5
3 10 37
4 100 41
36. Simulink Results (Case 2 – Condition 2)
36
Condition 2: Effect of time-varying delay and fixed data loss rate at 10%
Example 1: Data Loss Rate, r = 10% and
Time-varying delay= 0.001 seconds to
0.01 second
Example 2: Data Loss Rate, r = 10%
and Constant time-delay = 0.01
seconds to 0.1 second
37. Simulink Results (Case 2 – Condition 2)
37
Condition 2: Effect of time-varying delay and fixed data loss rate at 10%
Example 3: Data Loss Rate, r = 10%
and Time-varying delay= 0.001
seconds to 0.1 second
Example 4: Data Loss Rate, r = 10%
and Constant time-delay = 0.001
second to 0.5 second
38. Simulink Results (Case 2 – Condition 2)
38
Summary of the effect of increase in range of time-varying delay at
fixed data loss rate of 10% on consensus time.
Example
Time-Varying
Delay Range
(milliseconds)
Consensus Time
(seconds)
1 1-10 34
2 10-100 36
3 1-100 46
4 1-500 103
39. Simulink Results (Case 3)
39
Case 3: Effect of increase in number of agents on consensus of MAS
The directed graph topology for this case is,
The adjacency and Laplacian matrix are:
40. Simulink Results (Case 3 – Condition 1)
40
Condition 1: Effect of increase in number of agents with no time delay
and 10 % Data Loss Rate
41. Simulink Results (Case 3 – Condition 2)
41
Condition 2: Effect of increase in number of agents with constant time
delay of 1 millisecond and 10 % Data Loss Rate
42. Simulink Results (Case 3)
42
Summary of the effect of increase in number of agents with a no time
delay case and a constant time delay case.
43. Experimental Setup
43
Omron-Adept Pioneer P3-DX Mobile Robot
16 Ultrasonic Sensors (8 Front and 8 Rear)
Max Speed: 1.6 m/s
Max Payload: 23 kg
Motor with 500 tick encoder
Three hot swappable 9Ah sealed batteries
Source: http://www.cyberbotics.com/
49. Conclusions
• A novel consensus algorithm for the MAS in the event of communication
link failure over the network was developed and tested.
• The permissible value of data loss rate that can be permissible is 20%
though it can be observed that the consensus is still possible for higher
percentages of data loss rates.
• The consensus time for higher data loss rates are not feasible, for which
there needs to be threshold range of consensus time within which the
consensus if achieved should be treated as feasible.
• For, constant time-delay, the consensus time is permissible till 30 to 40
seconds.
49
50. Conclusions
• Increasing the data loss rate increases the consensus time, but the feasible
result was observed till 10% data loss rate.
• The experimental setup was also carried out at data loss rate of 10%.
• For constant time-delay, the increase in value of time delay increases the
consensus time. For the considered system, the permissible value of time
delay is limited to the sampling time i.e. 0.1 second or 100 milliseconds.
50
51. Future Works
• Can be applied to higher order dynamics.
• Time delays higher than the sampling period should be considered
as condition for controller design.
• Switching topology case can be considered.
51
52. Author Publication List
Conference Paper:
A. Pawar and Y.J. Pan, "Leader-following Consensus Control of Multi-
Agent Systems with Communication Delays and Random Packet Loss",
In Proceedings of the IEEE American Control Conference, June 2016, Boston,
USA, pp.4464-4469.
Journal Paper:
X. Gong, Y.J. Pan and A. Pawar, "A Novel Leader Following Consensus
Approach for Multi-Agent Systems with Data Loss", International Journal of
Control, Automation and Systems, Accepted, April 2016.
52