Abstract: Robust stability and performance are the two most
basic features of feedback control systems. The harmonic balance
analysis technique enables to analyze the stability of limit cycles
arising from a neural network control based system operating over
nonlinear plants. In this work a robust stability analysis based on the
harmonic balance is presented and applied to a neural based control
of a non-linear binary distillation column with unstructured
uncertainty. We develop ways to describe uncertainty in the form of
neglected nonlinear dynamics and high harmonics for the plant and
controller respectively. Finally, conclusions about the performance of
the neural control system are discussed using the Nyquist stability
margin together with the structured singular values of the uncertainty
as a robustness measure.
Abstract: It has proved that nonlinear diffusion and bilateral
filtering (BF) have a closed connection. Early effort and contribution
are to find a generalized representation to link them by using adaptive
filtering. In this paper a new further relationship between nonlinear
diffusion and bilateral filtering is explored which pays more attention
to numerical calculus. We give a fresh idea that bilateral filtering can
be accelerated by multigrid (MG) scheme which likes the nonlinear
diffusion, and show that a bilateral filtering process with large kernel
size can be approximated by a nonlinear diffusion process based on
full multigrid (FMG) scheme.
Abstract: Fault detection determines faultexistence and detecting
time. This paper discusses two layered fault detection methods to
enhance the reliability and safety. Two layered fault detection methods
consist of fault detection methods of component level controllers and
system level controllers. Component level controllers detect faults by
using limit checking, model-based detection, and data-driven
detection and system level controllers execute detection by stability
analysis which can detect unknown changes. System level controllers
compare detection results via stability with fault signals from lower
level controllers. This paper addresses fault detection methods via
stability and suggests fault detection criteria in nonlinear systems. The
fault detection method applies tothe hybrid control unit of a military
hybrid electric vehicleso that the hybrid control unit can detect faults
of the traction motor.
Abstract: Superelastic Shape Memory Alloy (SMA) is accepted
when it used as connection in steel structures. The seismic behaviour
of steel frames with SMA is being assessed in this study. Three eightstorey
steel frames with different SMA systems are suggested, the
first one of which is braced with diagonal bracing system, the second
one is braced with nee bracing system while the last one is which the
SMA is used as connection at the plastic hinge regions of beams.
Nonlinear time history analyses of steel frames with SMA subjected
to two different ground motion records have been performed using
Seismostruct software. To evaluate the efficiency of suggested
systems, the dynamic responses of the frames were compared. From
the comparison results, it can be concluded that using SMA element
is an effective way to improve the dynamic response of structures
subjected to earthquake excitations. Implementing the SMA braces
can lead to a reduction in residual roof displacement. The shape
memory alloy is effective in reducing the maximum displacement at
the frame top and it provides a large elastic deformation range. SMA
connections are very effective in dissipating energy and reducing the
total input energy of the whole frame under severe seismic ground
motion. Using of the SMA connection system is more effective in
controlling the reaction forces at the base frame than other bracing
systems. Using SMA as bracing is more effective in reducing the
displacements. The efficiency of SMA is dependant on the input
wave motions and the construction system as well.
Abstract: This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.
Abstract: To enhance installation security, a LNG storage in Rudong of Jiangsu province was adopted as a practical work, and it was analyzed by nonlinear finite element method to research overall and local stability performance, as well as the stress and deformation under the action of wind load and self-weight. Results indicate that deformation is tiny when steel mesh maintains as an overall ring, and stress caused by vertical bending moment and tension of bottom tie wire are also in the safe range. However, axial forces of lap reinforcement in adjacent steel mesh exceed the ultimate bearing capacity of tie wire. Hence, tie wires are ruptured; single mesh loses lateral connection and turns into monolithic status as the destruction of overall structure. Further more, monolithic steel mesh is led to collapse by the damage of bottom connection. So, in order to prevent connection failure and enhance installation security, the overlapping parts of steel mesh should be taken more reliable measures.
Abstract: The optimal control is one of the possible controllers
for a dynamic system, having a linear quadratic regulator and using
the Pontryagin-s principle or the dynamic programming method .
Stochastic disturbances may affect the coefficients (multiplicative
disturbances) or the equations (additive disturbances), provided that
the shocks are not too great . Nevertheless, this approach encounters
difficulties when uncertainties are very important or when the probability
calculus is of no help with very imprecise data. The fuzzy
logic contributes to a pragmatic solution of such a problem since it
operates on fuzzy numbers. A fuzzy controller acts as an artificial
decision maker that operates in a closed-loop system in real time.
This contribution seeks to explore the tracking problem and control
of dynamic macroeconomic models using a fuzzy learning algorithm.
A two inputs - single output (TISO) fuzzy model is applied to the
linear fluctuation model of Phillips and to the nonlinear growth model
of Goodwin.
Abstract: In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) for nonlinear systems with constrained input. Constant terms which arise from section wise linearization of a given nonlinear system are treated as coefficients of a stable zero dynamics.Parameters included in the control are suboptimally selectedby extremizing a combination of Hamiltonian and Lyapunov functions with the aid of the 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: This paper proposes an adaptive sliding mode
controller which combines adaptive control and sliding
mode control to control a nonlinear robotic manipulator
with uncertain parameters. We use an adaptive algorithm
based on the concept of sliding mode control to alleviate the
chattering phenomenon of control input. Adaptive laws are
developed to obtain the gain of switching input and the
boundary layer parameters. The stability and convergence
of the robotic manipulator control system are guaranteed
by applying the Lyapunov theorem. Simulation results
demonstrate that the chattering of control input can be
alleviated effectively. The proposed controller scheme can
assure robustness against a large class of uncertainties and
achieve good trajectory tracking performance.
Abstract: In this paper, an magnetorheological (MR) mount with
fuzzy sliding mode controller (FSMC) is studied for vibration
suppression when the system is subject to base excitations. In recent
years, magnetorheological fluids are becoming a popular material in
the field of the semi-active control. However, the dynamic equation of
an MR mount is highly nonlinear and it is difficult to identify. FSMC
provides a simple method to achieve vibration attenuation of the
nonlinear system with uncertain disturbances. This method is capable
of handling the chattering problem of sliding mode control effectively
and the fuzzy control rules are obtained by using the Lyapunov
stability theory. The numerical simulations using one-dimension and
two-dimension FSMC show effectiveness of the proposed controller
for vibration suppression. Further, the well-known skyhook control
scheme and an adaptive sliding mode controller are also included in
the simulation for comparison with the proposed FSMC.
Abstract: Behavior of dams against the seismic loads has been
studied by many researchers. Most of them proposed new numerical
methods to investigate the dam safety. In this paper, to study the
effect of nonlinear parameters of concrete in gravity dams, a twodimensional
approach was used including the finite element method,
staggered method and smeared crack approach. Effective parameters
in the models are physical properties of concrete such as modulus of
elasticity, tensile strength and specific fracture energy. Two different
models were used in foundation (mass-less and massed) in order to
determine the seismic response of concrete gravity dams. Results
show that when the nonlinear analysis includes the dam- foundation
interaction, the foundation-s mass, flexibility and radiation damping
are important in gravity dam-s response.
Abstract: This paper deals with infinite time horizon fuzzy Economic Order Quantity (EOQ) models for deteriorating items with
stock dependent demand rate and nonlinear holding costs by taking deterioration rate θ0 as a triangular fuzzy number (θ0 −δ 1, θ0, θ0 +δ 2), where 1 2 0 0
Abstract: In this study, the performance of a high-frequency arc
welding machine including a two-switch inverter is analyzed. The
control of the system is achieved using two different control
techniques i- fuzzy logic control (FLC) ii- state space averaging
based sliding control. Fuzzy logic control does not need accurate
mathematical model of a plant and can be used in nonlinear
applications. The second method needs the mathematical model of
the system. In this method the state space equations of the system are
derived for two different “on" and “off" states of the switches. The
derived state equations are combined with the sliding control rule
considering the duty-cycle of the converter. The performance of the
system is analyzed by simulating the system using SIMULINK tool
box of MATLAB. The simulation results show that fuzzy logic
controller is more robust and less sensitive to parameter variations.
Abstract: The purpose of this paper is to present the design and
instrumentation of a new benchmark multivariable nonlinear control
laboratory. The mathematical model of this system may be used to
test the applicability and performance of various nonlinear control
procedures. The system is a two degree-of-freedom robotic arm with
soft and hard (discontinuous) nonlinear terms. Two novel
mechanisms are designed to allow the implementation of adjustable
Coulomb friction and backlash.
Abstract: This paper deals with a nonlinear fractional differential equation with integral boundary condition of the following form: Dαt x(t) = f(t, x(t),Dβ t x(t)), t ∈ (0, 1), x(0) = 0, x(1) = 1 0 g(s)x(s)ds, where 1 < α ≤ 2, 0 < β < 1. Our results are based on the Schauder fixed point theorem and the Banach contraction principle.
Abstract: In today-s competitive market, most companies
develop manufacturing systems that can help in cost reduction and
maximum quality. Human issues are an important part of
manufacturing systems, yet most companies ignore their effects on
production performance. This paper aims to developing an integrated
workforce planning system that incorporates the human being.
Therefore, a multi-objective mixed integer nonlinear programming
model is developed to determine the amount of hiring, firing,
training, overtime for each worker type. This paper considers a
workforce planning model including human aspects such as skills,
training, workers- personalities, capacity, motivation, and learning
rates. This model helps to minimize the hiring, firing, training and
overtime costs, and maximize the workers- performance. The results
indicate that the workers- differences should be considered in
workforce scheduling to generate realistic plans with minimum costs.
This paper also investigates the effects of human learning rates on the
performance of the production systems.
Abstract: In this paper, different nonlinear dynamics analysis techniques are employed to unveil the rich nonlinear phenomena of the electromagnetic system. In particular, bifurcation diagrams, time responses, phase portraits, Poincare maps, power spectrum analysis, and the construction of basins of attraction are all powerful and effective tools for nonlinear dynamics problems. We also employ the method of Lyapunov exponents to show the occurrence of chaotic motion and to verify those numerical simulation results. Finally, two cases of a chaotic electromagnetic system being effectively controlled by a reference signal or being synchronized to another nonlinear electromagnetic system are presented.
Abstract: An exploration in the competency of the optical
multilevel Mapping Multiplexing Technique (MMT) system in
tolerating to the impact of nonlinearities as Self Phase Modulation
(SPM) during the presence of dispersion compensation methods. The
existence of high energy pulses stimulates deterioration in the chirp
compression process attained by SPM which introduces an upper
power boundary limit. An evaluation of the post and asymmetric prepost
fiber compensation methods have been deployed on the MMT
system compared with others of the same bit rate modulation formats.
The MMT 40 Gb/s post compensation system has 1.4 dB
enhancements to the 40 Gb/s 4-Arysystem and less than 3.9 dB
penalty compared to the 40 Gb/s OOK-RZsystem. However, the
optimized Pre-Post asymmetric compensation has an enhancement of
4.6 dB compared to the Post compensation MMT configuration for a
30% pre compensation dispersion.
Abstract: Midpoint filter is quite effective in recovering the
images confounded by the short-tailed (uniform) noise. It, however,
performs poorly in the presence of additive long-tailed (impulse)
noise and it does not preserve the edge structures of the image
signals. Median smoother discards outliers (impulses) effectively, but
it fails to provide adequate smoothing for images corrupted with nonimpulse
noise. In this paper, two nonlinear techniques for image
filtering, namely, New Filter I and New Filter II are proposed based
on a nonlinear high-pass filter algorithm. New Filter I is constructed
using a midpoint filter, a highpass filter and a combiner. It suppresses
uniform noise quite well. New Filter II is configured using an alpha
trimmed midpoint filter, a median smoother of window size 3x3, the
high pass filter and the combiner. It is robust against impulse noise
and attenuates uniform noise satisfactorily. Both the filters are shown
to exhibit good response at the image boundaries (edges). The
proposed filters are evaluated for their performance on a test image
and the results obtained are included.
Abstract: In this paper we present a substantiation of a new
Laguerre-s type iterative method for solving of a nonlinear
polynomial equations systems with real coefficients. The problems of
its implementation, including relating to the structural choice of
initial approximations, were considered. Test examples demonstrate
the effectiveness of the method at the solving of many practical
problems solving.