Abstract: In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.
Abstract: The system of ordinary nonlinear differential
equations describing sliding velocity during impact with friction for a
three-dimensional rigid-multibody system is developed. No analytical
solutions have been obtained before for this highly nonlinear system.
Hence, a power series solution is proposed. Since the validity of this
solution is limited to its convergence zone, a suitable time step is
chosen and at the end of it a new series solution is constructed. For a
case study, the trajectory of the sliding velocity using the proposed
method is built using 6 time steps, which coincides with a Runge-
Kutta solution using 38 time steps.
Abstract: A cyclostationary Gaussian linearization method is
formulated for investigating the time average response of nonlinear
system under sinusoidal signal and white noise excitation. The
quantitative measure of cyclostationary mean, variance, spectrum of
mean amplitude, and mean power spectral density of noise are
analyzed. The qualitative response behavior of stochastic jump and
bifurcation are investigated. The validity of the present approach in
predicting the quantitative and qualitative statistical responses is
supported by utilizing Monte Carlo simulations. The present analysis
without imposing restrictive analytical conditions can be directly
derived by solving non-linear algebraic equations. The analytical
solution gives reliable quantitative and qualitative prediction of mean
and noise response for the Duffing system subjected to both sinusoidal
signal and white noise excitation.
Abstract: This paper presents a novel integrated hybrid
approach for fault diagnosis (FD) of nonlinear systems. Unlike most
FD techniques, the proposed solution simultaneously accomplishes
fault detection, isolation, and identification (FDII) within a unified
diagnostic module. At the core of this solution is a bank of adaptive
neural parameter estimators (NPE) associated with a set of singleparameter
fault models. The NPEs continuously estimate unknown
fault parameters (FP) that are indicators of faults in the system. Two
NPE structures including series-parallel and parallel are developed
with their exclusive set of desirable attributes. The parallel scheme is
extremely robust to measurement noise and possesses a simpler, yet
more solid, fault isolation logic. On the contrary, the series-parallel
scheme displays short FD delays and is robust to closed-loop system
transients due to changes in control commands. Finally, a fault
tolerant observer (FTO) is designed to extend the capability of the
NPEs to systems with partial-state measurement.
Abstract: This paper attempts to define the validity domain of
LSDP (Loop Shaping Design Procedure) controller system, by
determining the suitable uncertainty region, so that linear system be
stable. Indeed the LSDP controller cannot provide stability for any
perturbed system. For this, we will use the gap metric tool that is
introduced into the control literature for studying robustness
properties of feedback systems with uncertainty. A 2nd order electric
linear system example is given to define the validity domain of LSDP
controller and effectiveness gap metric.
Abstract: Elastomeric dielectric material has recently become a
new alternative for actuator technology. The characteristics of
dielectric elastomers placed between two electrodes to withstand
large strain when electrodes are charged has attracted the attention of
many researcher to study this material for actuator technology. Thus,
in the past few years Danfoss Ventures A/S has established their own
dielectric electro-active polymer (DEAP), which was called
PolyPower.
The main objective of this work was to investigate the dynamic
characteristics for vibration control of a PolyPower actuator folded in
‘pull’ configuration. A range of experiments was carried out on the
folded actuator including passive (without electrical load) and active
(with electrical load) testing. For both categories static and dynamic
testing have been done to determine the behavior of folded DEAP
actuator.
Voltage-Strain experiments show that the DEAP folded actuator is
a non-linear system. It is also shown that the voltage supplied has no
effect on the natural frequency. Finally, varying AC voltage with
different amplitude and frequency shows the parameters that
influence the performance of DEAP folded actuator. As a result, the
actuator performance dominated by the frequency dependence of the
elastic response and was less influenced by dielectric properties.
Abstract: In this paper, we present preconditioned generalized
accelerated overrelaxation (GAOR) methods for solving certain
nonsingular linear system. We compare the spectral radii of the
iteration matrices of the preconditioned and the original methods. The
comparison results show that the preconditioned GAOR methods
converge faster than the GAOR method whenever the GAOR method
is convergent. Finally, we give two numerical examples to confirm our
theoretical results.
Abstract: Many problems in science and engineering field require
the solution of shifted linear systems with multiple right hand
sides and multiple shifts. To solve such systems efficiently, the
implicitly restarted global GMRES algorithm is extended in this
paper. However, the shift invariant property could no longer hold over
the augmented global Krylov subspace due to adding the harmonic
Ritz matrices. To remedy this situation, we enforce the collinearity
condition on the shifted system and propose shift implicitly restarted
global GMRES. The new method not only improves the convergence
but also has a potential to simultaneously compute approximate
solution for the shifted systems using only as many matrix vector
multiplications as the solution of the seed system requires. In
addition, some numerical experiments also confirm the effectiveness
of our method.
Abstract: Analysis of real life problems often results in linear
systems of equations for which solutions are sought. The method to
employ depends, to some extent, on the properties of the coefficient
matrix. It is not always feasible to solve linear systems of equations
by direct methods, as such the need to use an iterative method
becomes imperative. Before an iterative method can be employed
to solve a linear system of equations there must be a guaranty that
the process of solution will converge. This guaranty, which must
be determined apriori, involve the use of some criterion expressible
in terms of the entries of the coefficient matrix. It is, therefore,
logical that the convergence criterion should depend implicitly on the
algebraic structure of such a method. However, in deference to this
view is the practice of conducting convergence analysis for Gauss-
Seidel iteration on a criterion formulated based on the algebraic
structure of Jacobi iteration. To remedy this anomaly, the Gauss-
Seidel iteration was studied for its algebraic structure and contrary
to the usual assumption, it was discovered that some property of the
iteration matrix of Gauss-Seidel method is only diagonally dominant
in its first row while the other rows do not satisfy diagonal dominance.
With the aid of this structure we herein fashion out an improved
version of Gauss-Seidel iteration with the prospect of enhancing
convergence and robustness of the method. A numerical section is
included to demonstrate the validity of the theoretical results obtained
for the improved Gauss-Seidel method.
Abstract: Control of a semi-batch polymerization reactor using
an adaptive radial basis function (RBF) neural network method is
investigated in this paper. A neural network inverse model is used to
estimate the valve position of the reactor; this method can identify the
controlled system with the RBF neural network identifier. The
weights of the adaptive PID controller are timely adjusted based on
the identification of the plant and self-learning capability of RBFNN.
A PID controller is used in the feedback control to regulate the actual
temperature by compensating the neural network inverse model
output. Simulation results show that the proposed control has strong
adaptability, robustness and satisfactory control performance and the
nonlinear system is achieved.
Abstract: In recent years, many researchers are involved in the
field of fuzzy theory. However, there are still a lot of issues to be
resolved. Especially on topics related to controller design such as the
field of robot, artificial intelligence, and nonlinear systems etc.
Besides fuzzy theory, algorithms in swarm intelligence are also a
popular field for the researchers. In this paper, a concept of utilizing
one of the swarm intelligence method, which is called Bacterial-GA
Foraging, to find the stabilized common P matrix for the fuzzy
controller system is proposed. An example is given in in the paper, as
well.
Abstract: In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of
nonlinear systems with constrained input is presented. When designed
the control, a constant term which arises from linearization of a
given nonlinear system is treated as a coefficient of a stable zero
dynamics. Parameters of the control are suboptimally selected by
maximizing the stable region in the sense of Lyapunov with the aid
of a genetic algorithm. This approach is applied to a field excitation
control problem of power system to demonstrate the splendidness
of the AACC. Simulation results show that the new controller can
improve performance remarkably well.
Abstract: In this paper, the stability analysis of a GA-Based adaptive fuzzy sliding model controller for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving FLC rules. Then, FLC rules and the consequent parameter are decided on via an Evolved Bat Algorithm (EBA). After this, we guarantee a new tracking performance inequality for the control system. The tracking problem is characterized to solve an eigenvalue problem (EVP). Next, an adaptive fuzzy sliding model controller (AFSMC) is proposed to stabilize the system so as to achieve good control performance. Lyapunov’s direct method can be used to ensure the stability of the nonlinear system. It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality (LMI) problem. Finally, a numerical simulation is provided to demonstrate the control methodology.
Abstract: The design of a feedback controller, so as to minimize a given performance criterion, for a general non-linear dynamical system is difficult; if not impossible. But for a large class of non-linear dynamical systems, the open loop control that minimizes a performance criterion can be obtained using calculus of variations and Pontryagin’s minimum principle. In this paper, the open loop optimal trajectories, that minimizes a given performance measure, is used to train the neural network whose inputs are state variables of non-linear dynamical systems and the open loop optimal control as the desired output. This trained neural network is used as the feedback controller. In other words, attempts are made here to solve the “inverse optimal control problem” by using the state and control trajectories that are optimal in an open loop sense.
Abstract: This paper presents a nonparametric identification of
continuous-time nonlinear systems by using a Gaussian process
(GP) model. The GP prior model is trained by artificial bee colony
algorithm. The nonlinear function of the objective system is estimated
as the predictive mean function of the GP, and the confidence
measure of the estimated nonlinear function is given by the predictive
covariance of the GP. The proposed identification method is applied
to modeling of a simplified electric power system. Simulation results
are shown to demonstrate the effectiveness of the proposed method.
Abstract: When we prefer to make the data secure from various attacks and fore integrity of data, we must encrypt the data before it is transmitted or stored. This paper introduces a new effective and lossless image encryption algorithm using a natural logarithmic function. The new algorithm encrypts an image through a three stage process. In the first stage, a reference natural logarithmic function is generated as the foundation for the encryption image. The image numeral matrix is then analyzed to five integer numbers, and then the numbers’ positions are transformed to matrices. The advantages of this method is useful for efficiently encrypting a variety of digital images, such as binary images, gray images, and RGB images without any quality loss. The principles of the presented scheme could be applied to provide complexity and then security for a variety of data systems such as image and others.
Abstract: Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. The problem of identifying Hammerstein-Wiener systems is addressed in the presence of linear subsystem of structure totally unknown and polynomial input and output nonlinearities. Presently, the system nonlinearities are allowed to be noninvertible. The system identification problem is dealt by developing a two-stage frequency identification method. First, the parameters of system nonlinearities are identified. In the second stage, a frequency approach is designed to estimate the linear subsystem frequency gain. All involved estimators are proved to be consistent.
Abstract: This paper deals with the direct torque control (DTC) of the induction motor. This type of control allows decoupling control between the flux and the torque without the need for a transformation of coordinates. However, as with other hysteresis-based systems, the classical DTC scheme represents a high ripple, in both the electromagnetic torque and the stator flux and a distortion in the stator current. As well, it suffers from variable switching frequency. To solve these problems various modifications, in conventional DTC scheme, have been made during the last decade. Indeed the DTC based on space vector modulation (SVM) has proved to generate very low ripples in torque and flux with constant switching frequency. It also shows almost the same dynamic performances as the classical DTC system. On the other hand, fuzzy logic is considered as an interesting alternative approach for its advantages: Analysis close to the exigencies of user, ability of nonlinear systems control, best dynamic performances and inherent quality of robustness.
Therefore, two fuzzy direct torque control approaches, for the induction motor fed by SVM-voltage source inverter, are proposed in this paper. By using these two approaches of DTC, the advantages of fuzzy logic control, space vector modulation, and direct torque control method are combined. The performances of these DTC schemes are evaluated through digital simulation using Matlab/Simulink platform and fuzzy logic tools. Simulation results illustrate the effectiveness and the superiority of the proposed Fuzzy DTC-SVM schemes in comparison to the classical DTC.
Abstract: In this paper, we present new preconditioned modified accelerated overrelaxation (MAOR) method for solving linear systems. We compare the spectral radii of the iteration matrices of the preconditioned and the original methods. The comparison results show that the preconditioned MAOR method converges faster than the MAOR method whenever the MAOR method is convergent. Finally, we give one numerical example to confirm our theoretical results.
Abstract: In this paper, we present new preconditioned generalized mixed-type splitting (GMTS) methods for solving weighted linear least square problems. We compare the spectral radii of the iteration matrices of the preconditioned and the original methods. The comparison results show that the preconditioned GMTS methods converge faster than the GMTS method whenever the GMTS method is convergent. Finally, we give a numerical example to confirm our theoretical results.