Abstract: This paper considers the control of the longitudinal
flight dynamics of an F-16 aircraft. The primary design objective
is model-following of the pitch rate q, which is the preferred
system for aircraft approach and landing. Regulation of the aircraft
velocity V (or the Mach-hold autopilot) is also considered, but
as a secondary objective. The problem is challenging because the
system is nonlinear, and also non-affine in the input. A sliding
mode controller is designed for the pitch rate, that exploits the
modal decomposition of the linearized dynamics into its short-period
and phugoid approximations. The inherent robustness of the SMC
design provides a convenient way to design controllers without gain
scheduling, with a steady-state response that is comparable to that
of a conventional polynomial based gain-scheduled approach with
integral control, but with improved transient performance. Integral
action is introduced in the sliding mode design using the recently
developed technique of “conditional integrators", and it is shown that
robust regulation is achieved with asymptotically constant exogenous
signals, without degrading the transient response. Through extensive
simulation on the nonlinear multiple-input multiple-output (MIMO)
longitudinal model of the F-16 aircraft, it is shown that the conditional
integrator design outperforms the one based on the conventional linear
control, without requiring any scheduling.
Abstract: An active suspension system has been proposed to
improve the ride comfort. A quarter-car 2 degree-of-freedom (DOF)
system is designed and constructed on the basis of the concept of a
four-wheel independent suspension to simulate the actions of an
active vehicle suspension system. The purpose of a suspension
system is to support the vehicle body and increase ride comfort. The
aim of the work described in the paper was to illustrate the
application of fuzzy logic technique to the control of a continuously
damping automotive suspension system. The ride comfort is
improved by means of the reduction of the body acceleration caused
by the car body when road disturbances from smooth road and real
road roughness.
The paper describes also the model and controller used in the
study and discusses the vehicle response results obtained from a
range of road input simulations. In the conclusion, a comparison of
active suspension fuzzy control and Proportional Integration
derivative (PID) control is shown using MATLAB simulations.
Abstract: In this paper by using the port-controlled Hamiltonian
(PCH) systems theory, a full-order nonlinear controlled model is first
developed. Then a nonlinear passivity-based robust adaptive control
(PBRAC) of switched reluctance motor in the presence of external
disturbances for the purpose of torque ripple reduction and
characteristic improvement is presented. The proposed controller
design is separated into the inner loop and the outer loop controller.
In the inner loop, passivity-based control is employed by using
energy shaping techniques to produce the proper switching function.
The outer loop control is employed by robust adaptive controller to
determine the appropriate Torque command. It can also overcome the
inherent nonlinear characteristics of the system and make the whole
system robust to uncertainties and bounded disturbances. A 4KW 8/6
SRM with experimental characteristics that takes magnetic saturation
into account is modeled, simulation results show that the proposed
scheme has good performance and practical application prospects.
Abstract: A Variable Structure Model Reference Adaptive Controller using state variables is proposed for a class of multi input-multi output systems. Adaptation law is of variable structure type and switching functions is designed based on stability requirements. Global exponential stability is proved based on Lyapunov criterion. Transient behavior is analyzed using sliding mode control and shows perfect model following at a finite time.
Abstract: This paper describes the design of a voltage based maximum power point tracker (MPPT) for photovoltaic (PV) applications. Of the various MPPT methods, the voltage based method is considered to be the simplest and cost effective. The major disadvantage of this method is that the PV array is disconnected from the load for the sampling of its open circuit voltage, which inevitably results in power loss. Another disadvantage, in case of rapid irradiance variation, is that if the duration between two successive samplings, called the sampling period, is too long there is a considerable loss. This is because the output voltage of the PV array follows the unchanged reference during one sampling period. Once a maximum power point (MPP) is tracked and a change in irradiation occurs between two successive samplings, then the new MPP is not tracked until the next sampling of the PV array voltage. This paper proposes an MPPT circuit in which the sampling interval of the PV array voltage, and the sampling period have been shortened. The sample and hold circuit has also been simplified. The proposed circuit does not utilize a microcontroller or a digital signal processor and is thus suitable for low cost and low power applications.
Abstract: The robot is a repeated task plant. The control of such
a plant under parameter variations and load disturbances is one of the
important problems. The aim of this work is to design Geno-Fuzzy
controller suitable for online applications to control single link rigid
robot arm plant. The genetic-fuzzy online controller (indirect
controller) has two genetic-fuzzy blocks, the first as controller, the
second as identifier. The identification method is based on inverse
identification technique. The proposed controller it tested in normal
and load disturbance conditions.
Abstract: In this paper we present a novel design of a wearable
electronic textile. After defining a special application, we used the
specifications of some low power, tiny elements including sensors,
microcontrollers, transceivers, and a fault tolerant special topology to
have the most reliability as well as low power consumption and
longer lifetime. We have considered two different conditions as
normal and bodily critical conditions and set priorities for using
different sensors in various conditions to have a longer effective
lifetime.
Abstract: This paper presents the approach to design the Auto-
Tuning PID controller for interactive Water Level Process using
integral step response. The Integral Step Response (ISR) is the
method to model a dynamic process which can be done easily,
conveniently and very efficiently. Therefore this method is advantage
for design the auto tune PID controller. Our scheme uses the root
locus technique to design PID controller. In this paper MATLAB is
used for modeling and testing of the control system. The
experimental results of the interacting water level process can be
satisfyingly illustrated the transient response and the steady state
response.
Abstract: This paper puts forward one kind of air-fuel ratio
control method with PI controller. With the help of
MATLAB/SIMULINK software, the mathematical model of air-fuel
ratio control system for distributorless CNG engine is constructed.
The objective is to maintain cylinder-to-cylinder air-fuel ratio at a
prescribed set point, determined primarily by the state of the Three-
Way-Catalyst (TWC), so that the pollutants in the exhaust are
removed with the highest efficiency. The concurrent control of airfuel
under transient conditions could be implemented by Proportional
and Integral (PI) controller. The simulation result indicates that the
control methods can easily eliminate the air/fuel maldistribution and
maintain the air/fuel ratio at the stochiometry within minimum
engine events.
Abstract: This paper presents an adaptive feedback linearization approach to derive helicopter. Ideal feedback linearization is defined for the cases when the system model is known. Adaptive feedback linearization is employed to get asymptotically exact cancellation for the inherent uncertainty in the knowledge of the given parameters of system. The control algorithm is implemented using the feedback linearization technique and adaptive method. The controller parameters are unknown where an adaptive control law aims to drive them towards their ideal values for providing perfect model matching between the reference model and the closed-loop plant model. The converged parameters of controller would then provide good estimates for the unknown plant parameters.
Abstract: Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, particle swarm optimization (PSO) technique is applied to design a robust power system stabilizer (PSS). The design problem of the proposed controller is formulated as an optimization problem and PSO is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions; disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. Further, all the simulations results are compared with a conventionally designed power system stabilizer to show the superiority of the proposed design approach.
Abstract: Utilization of various sensors has made it possible to
extend capabilities of industrial robots. Among these are vision
sensors that are used for providing visual information to assist robot
controllers. This paper presents a method of integrating a vision
system and a simulation program with an industrial robot. The vision
system is employed to detect a target object and compute its location
in the robot environment. Then, the target object-s information is sent
to the robot controller via parallel communication port. The robot
controller uses the extracted object information and the simulation
program to control the robot arm for approaching, grasping and
relocating the object. This paper presents technical details of system
components and describes the methodology used for this integration.
It also provides a case study to prove the validity of the methodology
developed.
Abstract: Heating systems are a necessity for regions which
brace extreme cold weather throughout the year. To maintain a comfortable temperature inside a given place, heating systems
making use of- Hydronic boilers- are used. The principle of a single
pipe system serves as a base for their working. It is mandatory for these heating systems to control the room temperature, thus
maintaining a warm environment. In this paper, the concept of regulation of the room temperature over a wide range is established
by using an Adaptive Fuzzy Controller (AFC). This fuzzy controller automatically detects the changes in the outside temperatures and
correspondingly maintains the inside temperature to a palatial value. Two separate AFC's are put to use to carry out this function: one to
determine the quantity of heat needed to reach the prospective temperature required and to set the desired temperature; the other to control the position of the valve, which is directly proportional to the
error between the present room temperature and the user desired temperature. The fuzzy logic controls the position of the valve as per
the requirement of the heat. The amount by which the valve opens or closes is controlled by 5 knob positions, which vary from minimum to maximum, thereby regulating the amount of heat flowing through the valve. For the given test system data, different de-fuzzifier
methods have been implemented and the results are compared. In order to validate the effectiveness of the proposed approach, a fuzzy controller has been designed by obtaining a test data from a real time
system. The simulations are performed in MATLAB and are verified with standard system data. The proposed approach can be implemented for real time applications.
Abstract: This paper features the proposed modeling and design
of a Robust Decentralized Periodic Output Feedback (RDPOF)
control technique for the active vibration control of smart flexible
multimodel Euler-Bernoulli cantilever beams for a multivariable
(MIMO) case by retaining the first 6 vibratory modes. The beam
structure is modeled in state space form using the concept of
piezoelectric theory, the Euler-Bernoulli beam theory and the Finite
Element Method (FEM) technique by dividing the beam into 4 finite
elements and placing the piezoelectric sensor / actuator at two finite
element locations (positions 2 and 4) as collocated pairs, i.e., as
surface mounted sensor / actuator, thus giving rise to a multivariable
model of the smart structure plant with two inputs and two outputs.
Five such multivariable models are obtained by varying the
dimensions (aspect ratios) of the aluminum beam, thus giving rise to
a multimodel of the smart structure system. Using model order
reduction technique, the reduced order model of the higher order
system is obtained based on dominant eigen value retention and the
method of Davison. RDPOF controllers are designed for the above 5
multivariable-multimodel plant. The closed loop responses with the
RDPOF feedback gain and the magnitudes of the control input are
observed and the performance of the proposed multimodel smart
structure system with the controller is evaluated for vibration control.
Abstract: This paper proposes the analysis and design of robust
fuzzy control to Stochastic Parametrics Uncertaint Linear systems.
This system type to be controlled is partitioned into several linear
sub-models, in terms of transfer function, forming a convex polytope,
similar to LPV (Linear Parameters Varying) system. Once defined the
linear sub-models of the plant, these are organized into fuzzy Takagi-
Sugeno (TS) structure. From the Parallel Distributed Compensation
(PDC) strategy, a mathematical formulation is defined in the frequency
domain, based on the gain and phase margins specifications,
to obtain robust PI sub-controllers in accordance to the Takagi-
Sugeno fuzzy model of the plant. The main results of the paper are
based on the robust stability conditions with the proposal of one
Axiom and two Theorems.
Abstract: Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.
Abstract: Modeling and vibration of a flexible link manipulator
with tow flexible links and rigid joints are investigated which can
include an arbitrary number of flexible links. Hamilton principle and
finite element approach is proposed to model the dynamics of
flexible manipulators. The links are assumed to be deflection due to
bending. The association between elastic displacements of links is
investigated, took into account the coupling effects of elastic motion
and rigid motion. Flexible links are treated as Euler-Bernoulli beams
and the shear deformation is thus abandoned. The dynamic behavior
due to flexibility of links is well demonstrated through numerical
simulation. The rigid-body motion and elastic deformations are
separated by linearizing the equations of motion around the rigid
body reference path. Simulation results are shown on for both
position and force trajectory tracking tasks in the presence of varying
parameters and unknown dynamics remarkably well. The proposed
method can be used in both dynamic simulation and controller
design.
Abstract: Markov games are a generalization of Markov
decision process to a multi-agent setting. Two-player zero-sum
Markov game framework offers an effective platform for designing
robust controllers. This paper presents two novel controller design
algorithms that use ideas from game-theory literature to produce
reliable controllers that are able to maintain performance in presence
of noise and parameter variations. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. Our approach
generates an optimal control policy for the agent (controller) via a
simple Linear Program enabling the controller to learn about the
unknown environment. The controller is facing an unknown
environment, and in our formulation this environment corresponds to
the behavior rules of the noise modeled as the opponent. Proposed
controller architectures attempt to improve controller reliability by a
gradual mixing of algorithmic approaches drawn from the game
theory literature and the Minimax-Q Markov game solution
approach, in a reinforcement-learning framework. We test the
proposed algorithms on a simulated Inverted Pendulum Swing-up
task and compare its performance against standard Q learning.
Abstract: This paper proposes the novel model order
formulation scheme to design a discrete PID controller for higher
order linear time invariant discrete systems. Modified PSO (MPSO)
based model order formulation technique has used to obtain the
successful formulated second order system. PID controller is tuned to
meet the desired performance specification by using pole-zero
cancellation and proposed design procedures. Proposed PID
controller is attached with both higher order system and formulated
second order system. System specifications are tabulated and closed
loop response is observed for stabilization process. The proposed
method is illustrated through numerical examples from literature.
Abstract: Fuzzy controllers are potential candidates for the
control of nonlinear, time variant and also complicated systems. Anti
lock brake system (ABS) which is a nonlinear system, may not be
easily controlled by classical control methods. An intelligent Fuzzy
control method is very useful for this kind of nonlinear system. A
typical antilock brake system (ABS) by sensing the wheel lockup,
releases the brakes for a short period of time, and then reapplies again
the brakes when the wheel spins up. In this paper, an intelligent fuzzy
ABS controller is designed to adjust slipping performance for variety
of roads. There are tow major sections in the proposing control
system. First section consists of tow Fuzzy-Logic Controllers (FLC)
providing optimal brake torque for both front and rear wheels.
Second section which is also a FLC provides required amount of slip
and torque references properties for different kind of roads.
Simulation results of our proposed intelligent ABS for three different
kinds of road show more reliable and better performance in compare
with two other break systems.