249 research outputs found
Effect of Random Parameter Switching on Commensurate Fractional Order Chaotic Systems
The paper explores the effect of random parameter switching in a fractional
order (FO) unified chaotic system which captures the dynamics of three popular
sub-classes of chaotic systems i.e. Lorenz, Lu and Chen's family of attractors.
The disappearance of chaos in such systems which rapidly switch from one family
to the other has been investigated here for the commensurate FO scenario. Our
simulation study show that a noise-like random variation in the key parameter
of the unified chaotic system along with a gradual decrease in the commensurate
FO is capable of suppressing the chaotic fluctuations much earlier than that
with the fixed parameter one. The chaotic time series produced by such random
parameter switching in nonlinear dynamical systems have been characterized
using the largest Lyapunov exponent (LLE) and Shannon entropy. The effect of
choosing different simulation techniques for random parameter FO switched
chaotic systems have also been explored through two frequency domain and three
time domain methods. Such a noise-like random switching mechanism could be
useful for stabilization and control of chaotic oscillation in many real-world
applications.Comment: 31 pages, 17 figures, 5 Table
Kriging based Surrogate Modeling for Fractional Order Control of Microgrids
This paper investigates the use of fractional order (FO) controllers for a
microgrid. The microgrid employs various autonomous generation systems like
wind turbine generator (WTG), solar photovoltaic (PV), diesel energy generator
(DEG) and fuel-cells (FC). Other storage devices like the battery energy
storage system (BESS) and the flywheel energy storage system (FESS) are also
present in the power network. An FO control strategy is employed and the FO-PID
controller parameters are tuned with a global optimization algorithm to meet
system performance specifications. A kriging based surrogate modeling technique
is employed to alleviate the issue of expensive objective function evaluation
for the optimization based controller tuning. Numerical simulations are
reported to prove the validity of the proposed methods. The results for both
the FO and the integer order (IO) controllers are compared with standard
evolutionary optimization techniques and the relative merits and demerits of
the kriging based surrogate modeling are discussed. This kind of optimization
technique is not only limited to this specific case of microgrid control but
also can be ported to other computationally expensive power system optimization
problems.Comment: 9 pages, 13 figures. appears in Smart Grid, IEEE Transactions on,
201
An Overview of Face Liveness Detection
Face recognition is a widely used biometric approach. Face recognition
technology has developed rapidly in recent years and it is more direct, user
friendly and convenient compared to other methods. But face recognition systems
are vulnerable to spoof attacks made by non-real faces. It is an easy way to
spoof face recognition systems by facial pictures such as portrait photographs.
A secure system needs Liveness detection in order to guard against such
spoofing. In this work, face liveness detection approaches are categorized
based on the various types techniques used for liveness detection. This
categorization helps understanding different spoof attacks scenarios and their
relation to the developed solutions. A review of the latest works regarding
face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness
detection approach.Comment: International Journal on Information Theory (IJIT), Vol.3, No.2,
April 201
Frequency Domain Design of Fractional Order PID Controller for AVR System Using Chaotic Multi-objective Optimization
A fractional order (FO) PID or FOPID controller is designed for an Automatic
Voltage Regulator (AVR) system with the consideration of contradictory
performance objectives. An improved evolutionary Non-dominated Sorting Genetic
Algorithm (NSGA-II), augmented with a chaotic Henon map is used for the
multi-objective optimization based design procedure. The Henon map as the
random number generator outperforms the original NSGA-II algorithm and its
Logistic map assisted version for obtaining a better design trade-off with an
FOPID controller. The Pareto fronts showing the trade-offs between the
different design objectives have also been shown for both the FOPID controller
and the conventional PID controller to enunciate the relative merits and
demerits of each. The design is done in frequency domain and hence stability
and robustness of the design is automatically guaranteed unlike the other time
domain optimization based controller design methods.Comment: 26 pages, 9 figure
Multi-objective optimization framework for networked predictive controller design
Networked Control Systems (NCSs) often suffer from random packet dropouts
which deteriorate overall system's stability and performance. To handle the ill
effects of random packet losses in feedback control systems, closed over
communication network, a state feedback controller with predictive gains has
been designed. To achieve improved performance, an optimization based
controller design framework has been proposed in this paper with Linear Matrix
Inequality (LMI) constraints, to ensure guaranteed stability. Different
conflicting objective functions have been optimized with Non-dominated Sorting
Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only
gives guaranteed closed loop stability in the sense of Lyapunov, even in the
presence of random packet losses, but also gives an optimization trade-off
between two conflicting time domain control objectives.Comment: 38 pages, 30 figure
Fractional Order Fuzzy Control of Nuclear Reactor Power with Thermal-Hydraulic Effects in the Presence of Random Network Induced Delay and Sensor Noise having Long Range Dependence
Nonlinear state space modeling of a nuclear reactor has been done for the
purpose of controlling its global power in load following mode. The nonlinear
state space model has been linearized at different percentage of reactor powers
and a novel fractional order (FO) fuzzy proportional integral derivative (PID)
controller is designed using real coded Genetic Algorithm (GA) to control the
reactor power level at various operating conditions. The effectiveness of using
the fuzzy FOPID controller over conventional fuzzy PID controllers has been
shown with numerical simulations. The controllers tuned with the highest power
models are shown to work well at other operating conditions as well; over the
lowest power model based design and hence are robust with respect to the
changes in nuclear reactor operating power levels. This paper also analyzes the
degradation of nuclear reactor power signal due to network induced random
delays in shared communication network and due to sensor noise while being
fed-back to the Reactor Regulating System (RRS). The effect of long range
dependence (LRD) which is a practical consideration for the stochastic
processes like network induced delay and sensor noise has been tackled by
optimum tuning of FO fuzzy PID controllers using GA, while also taking the
operating point shift into consideration.Comment: 33 pages, 19 figure
Performance Comparison of Optimal Fractional Order Hybrid Fuzzy PID Controllers for Handling Oscillatory Fractional Order Processes with Dead Time
Fuzzy logic based PID controllers have been studied in this paper,
considering several combinations of hybrid controllers by grouping the
proportional, integral and derivative actions with fuzzy inferencing in
different forms. Fractional order (FO) rate of error signal and FO integral of
control signal have been used in the design of a family of decomposed hybrid FO
fuzzy PID controllers. The input and output scaling factors (SF) along with the
integro-differential operators are tuned with real coded genetic algorithm (GA)
to produce optimum closed loop performance by simultaneous consideration of the
control loop error index and the control signal. Three different classes of
fractional order oscillatory processes with various levels of relative
dominance between time constant and time delay have been used to test the
comparative merits of the proposed family of hybrid fractional order fuzzy PID
controllers. Performance comparison of the different FO fuzzy PID controller
structures has been done in terms of optimal set-point tracking, load
disturbance rejection and minimal variation of manipulated variable or smaller
actuator requirement etc. In addition, multi-objective Non-dominated Sorting
Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal
trade-offs between the set point tracking and control signal, and the set point
tracking and load disturbance performance for each of the controller structure
to handle the three different types of processes.Comment: 31 pages, 20 figure
On the Mixed H2/H-infinity Loop Shaping Trade-offs in Fractional Order Control of the AVR System
This paper looks at frequency domain design of a fractional order (FO) PID
controller for an Automatic Voltage Regulator (AVR) system. Various performance
criteria of the AVR system are formulated as system norms and is then coupled
with an evolutionary multi-objective optimization (MOO) algorithm to yield
Pareto optimal design trade-offs. The conflicting performance measures consist
of the mixed H2/H-infinity designs for objectives like set-point tracking, load
disturbance and noise rejection, controller effort and as such are an
exhaustive study of various conflicting design objectives. A fuzzy logic based
mechanism is used to identify the best compromise solution on the Pareto
fronts. The advantages and disadvantages of using a FOPID controller over the
conventional PID controller, which are popular for industrial use, are
enunciated from the presented simulations. The relevance and impact of FO
controller design from the perspective of the dynamics of AVR control loop is
also discussed.Comment: 10 pages, 24 figures, 1 table, Accepted in IEEE Transactions on
Industrial Informatic
Design of Hybrid Regrouping PSO-GA based Sub-optimal Networked Control System with Random Packet Losses
In this paper, a new approach has been presented to design sub-optimal state
feedback regulators over Networked Control Systems (NCS) with random packet
losses. The optimal regulator gains, producing guaranteed stability are
designed with the nominal discrete time model of a plant using Lyapunov
technique which produces a few set of Bilinear Matrix Inequalities (BMIs). In
order to reduce the computational complexity of the BMIs, a Genetic Algorithm
(GA) based approach coupled with the standard interior point methods for LMIs
has been adopted. A Regrouping Particle Swarm Optimization (RegPSO) based
method is then employed to optimally choose the weighting matrices for the
state feedback regulator design that gets passed through the GA based stability
checking criteria i.e. the BMIs. This hybrid optimization methodology put
forward in this paper not only reduces the computational difficulty of the
feasibility checking condition for optimum stabilizing gain selection but also
minimizes other time domain performance criteria like expected value of the
set-point tracking error with optimum weight selection based LQR design for the
nominal system.Comment: 27 pages, 7 figure
A Strongly Consistent Sparse -means Clustering with Direct Penalization on Variable Weights
We propose the Lasso Weighted -means (--means) algorithm as a
simple yet efficient sparse clustering procedure for high-dimensional data
where the number of features () can be much larger compared to the number of
observations (). In the --means algorithm, we introduce a lasso-based
penalty term, directly on the feature weights to incorporate feature selection
in the framework of sparse clustering. --means does not make any
distributional assumption of the given dataset and thus, induces a
non-parametric method for feature selection. We also analytically investigate
the convergence of the underlying optimization procedure in --means and
establish the strong consistency of our algorithm. --means is tested on
several real-life and synthetic datasets and through detailed experimental
analysis, we find that the performance of the method is highly competitive
against some state-of-the-art procedures for clustering and feature selection,
not only in terms of clustering accuracy but also with respect to computational
time
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