1,721,092 research outputs found
Development of Model Order Reduction Methods for Linear System Control Design
No full textMathematical modeling and simulation are very important in science and engineering
applications at the beginning of design stage for analysis, design, and control. They
also allow virtual experiments when a practical experiment is either time-consuming,
too costly, impractical or difficult to execute. Several practical systems are with large
dimensionality. Model reduction is an important robust tool allowing for fast numerical
simulation of complicated models. It aims to replace a large scale system by an
approximate system of lower order, which not only preserves the basic input-output
behaviour of the original system but also requires significantly less effort. Low order
models result in several advantages such as they are easier to understand; computational
requirement is less and reduces hardware complexity, and help to make a feasible
controller design, etc. The basic motivation behind various model reduction methods
is to find an appropriate low-order model, such that it preserves input-output behavior
and essential properties of the original system with minimum error.
The development of reduction methods for the analysis and synthesis of high order
systems has been an area of active research during the last few decades. Various
investigations have been performed and number of methods are suggested for order
reduction in both time and frequency domain in the field of control system. However,
none of order reduction methods have been universally accepted which can be applied
to each system. Every method has its own advantages and limitations and applicable
only for specific applications.
In this work, order reduction methods are presented for the large-scale LTI control systems
in the frequency and time domain. Their basic fundamental, properties, benefits,
and limitations are discussed along with the relationship among different approaches
to identify the appropriate methods for the given application. The main objective of
this thesis is to develop model reduction methods in both frequency and time domain.
In the frequency domain, three reduction techniques are presented, whose central objective
focuses not only to retain the dominant modes of high order model but also
include the effect of each less dominant pole on the system behavior. Further, the
accuracy of the proposed method is validated using benchmark examples which exits
in the literature and also the proposed method is compared with the state of art
methods.
In the first proposed algorithm for order reduction, the problem of selecting poles is
removed by applying the advantages of time moment and Markov parameters which are specified as a subroutine of the poles and residues of original model having real and
complex poles. The coefficients of the numerator polynomial for reduced model are
obtained using a factor division algorithm. In the second and third proposed method,
the denominator of reduced model is constructed from the new clustering method
based on modified Lehmer measure in order to improve dominant pole cluster centers.
This method uses the concept of a dominant pole algorithm for the selection of poles
for the clustering. The selection of poles is important because it determines both
steady-state and transient information of the dynamical system. Pade approximation
and frequency response matching technique are used to find the parameters of the
numerator polynomial for second and third proposed method respectively.
Time domain reduction method employ the idea of balancing for both stable and unstable
large-scale continuous-time systems. The method uses a quantitative measure
criterion for the selection of dominant eigenvalues. These dominant eigenvalues are
used to form a new substructure matrix that retains the dominant modes (or may desirable
mode) of the original system. Keeping the dominant eigenvalues in a reduced
model allows greater accuracy since the retained eigenvalues provide a physical link to
the high order model and also guarantees stability if the original model is stable. The
proposed method is accomplished by system transformation using the Sylvester equation,
taking into account to preserve the dominant eigenvalues of the high order model.
Having obtained a transformed model, the reduced model is achieved by truncating
the non-dominant eigenvalues.
This work also presents the low order controller design of the higher order plant/process
using indirect methods. The implementational issues and aspects of the model order
reduction methods have been tested through controller design. The step responses of
the closed-loop system obtained from the original and the reduced model are compared
with the step response of the reference model. The step response of the overall controlled
system for the original system and the reduced model are found in agreement
to that of reference model. Order reduction gives benefits such as i) more numerically
efficient controller design process, ii) able to calculate simple control laws, and iii)
minimum computational efforts required in simulation problems.
The accuracy and effectiveness of the proposed method is measure through numerical
test examples and compared with state of art methods. The comparison is made by
calculating the error index (ISE) between the transient parts of the original and various
reduced models. All the numerical computations have been performed with Matlab
software package
Tuning of internal model control –proportional integral derivative controller for optimized control
No full textMaster of EngineeringTime delays are usually unavoidable in the engineering systems like mechanical and
electrical systems etc. The presence of delay causes unwanted impacts on the system
under consideration which imposes strict limitations on achievable or targeted
feedback performance in both continuous and discrete systems. The presence of the
delay complicates the design process as well. It makes continuous systems to be
infinite dimensional and also it significantly increases the dimensions in discrete
systems. As the internal model control based proportional integral derivative
controller are simple and robust to handle the model uncertainties and disturbances.
But they are less sensitive to noise than proportional integral derivative controller for
an actual process in industries. It results in only one tuning parameter which is closed
loop time constant λ internal model controller filter factor. It also provides a good
solution to the process with significant time delays which is actually the case with
working in real time environment. So in this thesis internal model control based
proportional integral derivative controller is designed. The Pade’s approximation for
the time delay has been used because most of the controller design based on different
methods can not be used with the delayed systems. While comparing the responses of
the transfer functions of different kinds of orders the internal model control based
proportional integral derivative controller will not give the same results as the internal
model control strategy because of approximation used for delay time. Also the
standard internal model filter from f(s) =1 / (λs + 1) shows good set point tracking.
Thus internal model control based proportional integral derivative controller is able
to compensate for disturbances and model uncertainty while open loop control is not.
Internal model control is also detuned to assure stability even if there is model
uncertainty.Electrical and Instrumentation Engineering, Thapar University, Patial
Approach to design proportional integral derivative controller using internal model control for optimisation of proposed process control
No full textMaster of Engineering-EICThe Internal Model Control (IMC)-based approach is one of the controller designing method used
in control applications in industries. It is because, for practical applications or an actual process in
industries PID controller algorithm is simple and robust to handle the model inaccuracies and hence
using IMC-PID tuning method a clear trade-off between closed-loop performance and robustness to
model inaccuracies is achieved with a single tuning parameter.
Also the IMC-PID controller allows good set-point tracking but sulky disturbance response
especially for the process with a small time-delay/time-constant ratio. But, for many process control
applications, disturbance rejection for the unstable processes is much more important than set point
tracking. Hence, controller design that emphasizes disturbance rejection rather than set point
tracking is an important design problem that has to be taken into consideration.
In this dissertation, we propose an optimum IMC filter to design an IMC-PID controller for better
set-point tracking of unstable processes. The proposed controller works for different values of the
filter tuning parameters to achieve the desired response As the IMC approach is based on pole zero
cancellation, methods which comprise IMC design principles result in a good set point responses.
However, the IMC results in a long settling time for the load disturbances for lag dominant
processes which are not desirable in the control industry.
Thus in our approach to IMC and IMC based PID controller to be used in industrial process control
applications, there exists the optimum filter structure for each specific process model to give the
best PID performance. For a given filter structure, as λ decreases, the inconsistency between the
ideal and the PID controller increases while the nominal IMC performance improves. It indicates
that an optimum λ value also exist which compromises these two effects to give the best
performance. Thus what we mean by the best filter structure is the filter that gives the best PID
performance for the optimum λ value.Electrical and Instrumentation Engineering, Thapar University, Patial
Swarm Intelligence Based Tuning of Controller Parameters for Concentration Control of Isothermal Continuous Stirred Tank Reactor
No full textBasically chemical reactor is a reactor which is used to perform chemical
operations. Chemical reactors are of many types like isothermal chemical reactor and
non-isothermal chemical reactor. In recent years control of process plant has gained a
widespread research interest. Multi input multi output control, RGA based decoupling
control are some of the advanced control techniques which are being used in industries.
The increasing complexity of modern control systems has emphasized the idea of
applying new approaches in order to solve design problems for different control
engineering applications. Proportional-Integral-Derivative (PID) control schemes have
been widely used in most of process control systems represented by chemical processes
for a long time. However, tuning of PID controller is a very complex task, involving a lot
of design and process dynamic aspect. Swarm intelligence, which has caught the eyes of
researchers due to its simplicity, low computational cost, and good performance, makes it
a possible choice for tuning of PID controllers, to increase their performance.
This dissertation discusses, in detail, the Particle Swarm Optimization (PSO)
algorithm, and its implementation in PID controller tuning to get the optimal tuning
parameters. The PID controller is used to control the product concentration of isothermal
CSTR. First of all mathematical modeling of the process is performed using
experimental plant data. After the mathematical modeling, different controllers are
designed to meet the control objective. PSO is used to optimally tune the PID controller
A Comparison of Different Controllers to Control Various Parameters of Continuous Stirred Tank Reactor
No full textM.E. (Electronic Instrumentation and Control)Control of various parameters in a chemical plant is one of the major challenges in industry. This thesis considers a chemical reactor, and controls various parameters of the reactor using different controllers. The thesis also compares the performance of different controllers by time domain analysis like transient response and error analysis. The thesis models the chemical reactor, implements PID controller, feedback and feed forward controller, hybrid fuzzy controller to control various parameters of the chemical reactor.Electrical and Instrumentation Engineering Departemnt, Thapar University, Patial
Data Driven Multivariate Technique for Fault Detection of Waste Water Treatment Plant
No full textM.E. (Electronic Instrumentation and Control)Classification of data originating from sensor in to two distinct categories (good and bad) is a challenging job and has a wide spread application. A lot of research is going on in this particular area. Because of the enhanced memory capacity of the present day computers, data logging has reached to a new level. The analyst has to classify the data according to their traits from the offline logged data. The whole task of collection of raw data, classification of data according to their traits involves different statistical as well as soft computational techniques. There are two types of classification algorithms like supervised classification and unsupervised classification. In supervised classification, the classification is done using neural network and in unsupervised classification the classification is done using different clustering algorithm.
This dissertation studies and evaluates the performance of different classification algorithm in a waste water treatment plant. First of all waste water treatment plant is taken in to consideration and data driven classification techniques are implemented to find out the healthy data and faulty data. The healthy and faulty data is classified using supervised and unsupervised classification.Electrical and Instrumentation Engineering Department, Tahapar University, Patial
Neuro Fuzzy Control of Robotic Arm Movement
No full textThis thesis first outlines the theory, historical background, and application of neural networks
and fuzzy logic. The review of neural networks and fuzzy logic is followed by a discussion of the
combination of the two technologies -- neuro-fuzzy techniques. The two tools have been
successfully combined to maximize their individual strengths and compensate for shortcomings.
A survey is given of previous work done in applying these technologies to control systems.
The problem of moving a robotic arm in the presence of an obstacle is discussed. In particular,
trajectory planning of a planar, redundant manipulator is studied. The primary weakness of
previous methods for determining acceptable trajectories is the massive amount of computer time
needed to obtain a solution. Neuro-fuzzy systems offer not only the benefit of the parallel nature
of its computations, but also the ability to learn the control of an arm by following a human's
example.
Several neuro-fuzzy controllers are trained using sample data obtained from a human's control of
a robotic arm. Their performance is quantified and compared. It is shown that the definition of
the fuzzy membership functions plays a significant role in the ability of the neuro-fuzzy
controller to learn and generalize. Possible directions for future work are suggested
Edge Preserving Image Compression Technique Using Feed Forward Neural Network
No full textME(EIC)With the growth of multimedia and internet, compression techniques have become the thrust
area in the fields of computers. Popularity of multimedia has led to the integration of various
types of computer data. Multimedia combines many data types like text, graphics, still
images, animation, audio and video. Image compression is a process of efficiently coding
digital image to reduce the number of bits required in representing image. Its purpose is to
reduce the storage space and transmission cost while maintaining good quality. Many
different image compression techniques currently exist for the compression of different types
of images. In the present research work back propagation neural network training algorithm
has been used. The neural network model has been trained and tested for the different types
of images. Back propagation neural network algorithm helps to increase the performance of
the system and to decrease the convergence time for the training of the neural network. The
aim of this work is to develop an edge preserving image compressing technique using one
hidden layer feed forward neural network of which the neurons are determined adaptively
.The processed image block is fed as a single input pattern while single output pattern has
been constructed from the original image unlike other neural network based technique where
multiple image blocks are fed to train the network. The initialization of weights between the
lone hidden layer by transforming pixel coordinates of the input pattern block into its
equivalent one dimensional representation. The initialization process exhibit better rate
convergence of the back propagation training algorithm as compare to the randomization of
initial weight. The proposed scheme has been demonstrated through several experiments
including lena, girl, cameraman and very promising results in compression as well as in
reconstructed image over convectional neural network based technique are obtained.EIE
Drowsiness Detection of Driver While Driving Using MatLab
No full textME-DissertationVarious investigations show that driver’s drowsiness is one of the main causes of road accidents. The development of technologies for preventing drowsiness at the time is a major challenge in the field of accident avoidance. The advance in computing technology has provided the means for building intelligent vehicle systems. The purpose of this study is to detect the drowsiness in drivers to prevent the accidents and to improve the safety on the highways. A system aiming at detecting driver drowsiness or fatigue on the basis of video analysis is presented. A real time face detection is implemented to locate driver’s face region. A method of detecting drowsiness in drivers is developed by using a camera that points directly towards the driver’s face and capture for the video. As a detection method, the system uses image processing technology to analyze images of the driver’s face taken with a video camera. The captured video is done, it is converted into number of frames of images and monitoring of the face region and eyes in order to detect drowsiness. The system is able to monitoring eyes and determines whether the eyes are in an open or shows signs of drowsiness. This detection system provides a noncontact technique for judging various level of alertness and facilitates early detection of a decline in alertness during drivingEIED, TU, Patial
Fingerprint Recognition and Analysis
No full textMESecurity systems are now computerized. Automated security systems are essential now. These days most of the banking transactions, use of cell phones and personal digital assistants (PDAs) are frequently performed. This rapid progress in personal communication system, wireless communication system and smart card technology in the society makes information more quick & realistic. Due to the growing importance of the information technology and the necessity of the protection and access restriction, reliable personal identification is necessary and essential condition also. There are three main methodologies when performing this verification. The security system can ask the user to provide some secret information known only to the user and the system or can ask the user to provide something only the user has access to, it could identify trait which is unique for the user. Identifying trait that is unique for the user is known as fingerprint recognition, hand geometry, iris, face and signature etc.
Reliable extraction of features from poor quality prints is the most challenging problem faced in the area of fingerprint recognition. Fingerprints are the oldest and most widely used form of biometric identification. Local characteristic called minutiae points represent fingerprints. This can be used as identification marks for fingerprint recognition. The goal of this thesis is to develop a complete system for fingerprint recognition through minutiae extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of image enhancement, image segmentation and binarization is first applied on fingerprints before they are evaluated. The combination of multiple methods comes from a wide investigation into research papers.
Minutias marking with special consideration of the triple branch counting and false minutiae removal methods are used in the work. Also some novel changes like segmentation using morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work. The minutiae based fingerprints recognition technique is studied in detail and implemented in MATLAB.EIE
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