m. tech. (electrical) curriculum structure specialization ...m. tech. (electrical) curriculum...

M. Tech. (Electrical) Curriculum Structure Specialization: Control System Engineering

(w. e. f. 2015-16)

List of Abbreviations

OEC- Institute level Open Elective Course PSMC – Program Specific Mathematics Course PCC- Program Core Course DEC- Department Elective Course LLC- Liberal Learning (Self learning) Course MLC- Mandatory Learning Course (Non-credit course) LC- Laboratory Course

Semester I

Sr. No.

Course Type/Code

Course Name Teaching Scheme

Credits L T P

1. OEC Engineering Optimization 3 -- -- 3 2. PSMC Linear System Theory & Design 3 -- -- 3 3. PCC Modeling of Dynamic System 3 -- -- 3 4. PCC Nonlinear Dynamic Systems 3 1 -- 4 5. PCC Digital Control System 3 1 -- 4 6. LC PG Simulation Lab -- -- 6 3 7. MLC Research Methodology 1 -- -- -- 8. MLC Humanities 1 -- -- --

Total 17 1 8 20

Semester II

Sr. No.

Course Code/Type


Credits L T P

1. PCC Sliding Mode Control 3 -- -- 3

2. PCC Multivariable Control System 3 1 -- 4 3. PCC Optimal Control System 3 -- -- 3

4. DEC

Elective – II

3 -- -- 3 a. Intelligent Control b. Model Predictive Control c. System Identification & Adaptive Control d. Any other course approved by DPPC

5. DEC

Elective – II

3 -- -- 3 a. Fractional Order Modeling & Control b. Control Related Estimations c. Power electronics & Control d. Any other course approved by DPPC

6. LC PG Hardware Lab Lab -- -- 6 3

7. MLC Intellectual Property Rights 1 -- -- --

8. LLC Liberal Learning Course -- -- -- 1

Total 16 1 6 20

Semester-III

Sr. No.

Course Code


Credits L T P

1. Dissertation Dissertation Phase – I -- -- -- 16

Total -- -- -- 16

Semester-IV

Sr. No.

Course Code


Credits L T P

1. Dissertation Dissertation Phase - II -- -- -- 18

Total -- -- -- 18

(PSMC) Linear System Theory & Design Teaching Scheme Lectures: 3 hrs/week

Examination Scheme T1, T2 – 20 marks each, End-Sem Exam - 60

Course Outcomes:

At the end of the course, students will demonstrate the able to 1. Understand Vector spaces of LTI Systems

2. Analyze LTI Systems

3. Design controller for LTI Systems

Syllabus Contents:

• Mathematical description of the systems,

• LTI systems, vector spaces, linear dependence,

• basis, representation of linear transformations with respect to a basis,

• inner product spaces basis, representation, orthogonalization,

• linear algebraic equations, similarity transformations,

• various forms, functions of a square matrix, state space solutions and

realization,

• stability, controllability and observability,

• minimal realizations and co-prime fractions,

• state feedback and state estimators, pole place placement

References:

1. Chi-Tsong Chen, ”Linear System Theory and Design”, Oxford University Press.

2. John S. Bay, ”Linear System Theory”.

3. Thomas Kailath,” Linear System”, Prentice Hall, 1990 4. Gillette, ”Computer Oriented Operation Research”, Mc-Graw Hill Publications.

5. K. Hoffman and R. Kunze, ”Linear Algebra”, Prentice-Hall (India), 1986.

6. G.H. Golub and C.F. Van Loan, ”Matrix Computations”, North Oxford Academic, 1983.

7. G.H. Golub and C.F. Van Loan, ”Matrix Computations”, North Oxford Academic, 1983.

(PCC) Nonlinear Dynamical Systems Teaching Scheme Lectures: 3 hrs/week, Tutorial: 1 hr/week


Course Outcomes:

At the end of the course, students will demonstrate the able to 1. Explore tools for stability analysis and response evaluation of control problems with

significant nonlinearities.

2. Compute the performance and stability of the system.

3. Identify the design problem and distinguish between the controls strategies

4. Correlate between design parameters and the system performance.

Syllabus Contents:

• Introduction to nonlinear systems, phase plane and describing function

methods for analysis of nonlinear systems

• Lyapunov stability: autonomous systems invariance principle, linear systems

and linearization, non-autonomous systems. linear time varying systems

• Linearization, nonlinear control systems design by feedback linearization,

input output linearization.

• systems analysis based on Lyapunov’s direct method (Krasovaskii’s method,

variable gradient method), converse theorems, centre manifold theorem,

region of attraction, stability of perturbed system, input to state stability

• Lyapunov like analysis using Barbalet’s lemma, advanced stability theory,

References:

1. H. K. Khalil, ”Nonlinear Systems”, Prentice Hall, 2001.

2. Jean-Jacques E. Slotine, Weiping Li, ”Applied nonlinear Control”, Prentice Hall, 1991.

3. M Vidyasagar, ”Nonlinear systems Analysis”, 2nd Edition, Prentice Hall, 1993.

4. Alberto Isidori, ”Nonlinear Control System”, Vol I and II, Springer, 1999.

(PCC) Modeling of Dynamical Systems Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the able to 1 Develop mathematical models of various engineering and physical systems using

classical and energy approach.

1. Demonstrate linearization techniques.

2. Analyze the model from control perspective.

Syllabus Contents:

• Modeling by first principle approach of simple mechanical, electrical, thermal,

chemical systems. Modeling by energy approach using Lagrangian and

Hamiltonian

• Linearization of nonlinear models, state space approach for analyzing the

dynamic models

• Modeling and analysis of some typical systems such medical disease and

treatment, rocket launcher, resource management etc.,

• Numerical models using impulse response, step response

References:

1. K. Ogata, ”System Dynamics”, Pearson Prentice-Hall, 4th Edition, 2004.

2. M. Gopal, ”Modern Control Systems Theory”, 2nd Edition, John Wiley, 1993

3. E.O. Doeblin, ”System Modeling and Response”, John Wiley and Sons, 1980.

4. Desai and Lalwani, ”Identification Techniques”, Tata McGraw Hill, 1977.

5. Goldstain ,”Classical Mechanics”.

(PCC) Digital Control Systems Teaching Scheme Lectures: 3 hrs/week Tutorial :1 hr/week


Course Outcomes:

At the end of the course, students will demonstrate the able to

1 Obtain discrete representation of LTI systems 2 Analyze stability of open loop and closed loop discrete system

3 Design and analyze Discrete Controller

4 Design state feedback controller and estimators.

Syllabus Contents:

• Discrete time systems , discretization, sampling, aliasing, choice of

sampling frequency, ZOH equivalent, state space models of discrete

systems.

• Z-Transform for analyzing discrete time systems, transfer function,

internal stability, design of discrete time control using conventional

methods, • stability of discrete time systems, state space analysis, pole placement

and observer

References:

1. K. Ogata,”Discrete Time Control Systems”, Prentice hall, 1995.

2. Kannan M. Moudgalya,”Digital Control”, John Wiley and Sons, 2004.

3. Kuo, Benjamin C, ”Digital Control Systems”, New York : Holt, Rinehart and Winston,

1980.

4. M. Gopal, ”Digital Control”, MacGraw Hill.

5. G. F. Franklin, J. D. Powell, M.L. Workman, Digital Control of Dynamic Systems, Addison-Wesley, Reading, MA, 1998.

(LC) PG Simulation Laboratory Teaching Scheme Practicals : 6 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the able to

1 Demonstrate use of advanced software tools for problem solving, designing

controller and analyzing the system performance.

2 Interpret simulation results.

Syllabus Contents:

Simulation experiments/assignments on the platform like MATLAB, SCILAB, ATP/EMTP, PSCAD, MAXWELL, LABVIEW etc. The problems will be related to the core subjects

(MLC) Research Methodology Teaching Scheme Lectures: 1 hrs/week

Examination Scheme End-Sem Exam - 50

Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Understand research problem formulation.

2. Analyze research related information

3. Follow research ethics

Syllabus Contents:

• Meaning of research problem, Sources of research problem, Criteria Characteristics

of a good research problem, Errors in selecting a research problem, Scope and

objectives of research problem. • Approaches of investigation of solutions for research problem, data collection,

analysis, interpretation, Necessary instrumentations

• Effective literature studies, approaches, analysis

• Plagiarism , Research ethics,

• Effective technical writing, how to write report, Paper

• Developing a Research Proposal, Format of research proposal, a presentation and

assessment by a review committee

References:

3. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for

science & engineering students’”

4. Wayne Goddard and Stuart Melville, “Research Methodology: An Introduction”

5. by Ranjit Kumar, 2 nd Edition , “Research Methodology: A Step by Step Guide for

beginners”

(MLC) Humanity Teaching Scheme Lectures: 1 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Understand the need, basic guidelines, content and process for value education.

2. Understand the harmony in the family, difference between respect and

differentiation.

3. Understand the harmony in nature, interconnectedness and mutual fulfillment in

nature, holistic perception of harmony.

4. Understand natural acceptance of human values, competence in professional

ethics.

Introduction to the scope and significance of learning Humanities & communication:

Comprehension, Written communication: Formal letters, CV, Reports, Paragraphs Grammar and Vocabulary building exercises Social Science and Development:

Indian and western concept, Process of social change in modern India, Impact of development of Science and technology on culture and civilization. Urban sociology and Industrial sociology, Social problems in India: overpopulated cities, no skilled farmers, unemployment, addictions and abuses, illiteracy, too much cash flow, stressful working schedules, nuclear families etc. Technology assessment and transfer:

Sociological problems of economic development and social change Assessment and transfer of technology, problems related with tech transfer with reference to India. Roles of an engineer in value formation and their effects on society.

References:

1. Mcmillan ,’English for everyone’ (India) Ltd.

2. Jude paramjit S and Sharma Satish K Ed, ‘dimensions of social change ‘

3. Raman Sharma,” Social Changes in India”

Semester II

(PCC) Multivariable Control Systems Teaching Scheme Lectures: 3 hrs/week Tuorial : 1 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1 Understand types of MIMO systems and models, their mathematical properties,

analyze the system to relate these properties to the physical properties of the

system.

2 Demonstrate the control design strategies and understand the purpose for

specific strategy to be applied.

3 Design the control algorithms for MIMO systems for desired performance and

stability.

4 Implement the control algorithms for MIMO systems on MATLAB-SIMULINK

platform and compute the performance.

Syllabus Contents:

• Examples of multivariable control systems, state space, polynomial and

stable fraction models, polynomial matrices, transmission zero,

• Solution of state equations, contro l lab i l i ty , observability and computations

involved in their analysis.

• Realization th eo ry of multivariable systems and algorithms, stability and

stabilizability.

• Pole placement, observer design and stabilization theory, minimal realization,

frequency domain design, decoupling,

• Model matching, spectral factorizations of systems, solution of the Ricatti

equation, b a lan ce d realizations and their computations.

References:

1. Y.S.Apte, ”Linear multivariable control system”.

2. W.M.Wonham, ”Multivariable control systems”.

3. C.T.Chen, ”Linear system theory and design”, 3rd edition, Oxford 1999.

4. John Bay,”Fundamentals of linear state space systems”, McGraw Hill, 1998.

5. Wilson Rugh, ”Linear system theory”, 2nd edition, Prentice Hall, 1996.

H.H.Rosenbrock, ”Computer aided control system design

(PCC) Optimal Control Systems Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1 Solve problems on Calculus of Variation

2 Demonstrate concept of LQR Design and Dynamic programming techniques.

3 Demonstrate certain examples in MATLAB

Syllabus Contents:

• Introduction, static and dynamic optimization, parameter optimization, calculus

of variations : problems of Lagrange, Mayer and Bolza, Euler-Language equation

and transversality conditions,

• Lagrange multipliers, Pontryagins maximum principle; theory; application to

minimum time, energy and control effort problems, and terminal control

problem,

• dynamic programming : Bellman’s principle of optimality, multistage decision

processes, application to optimal control,

• linear regulator problem : matrix Riccati equation and its solution, tracking

problem, computational methods in optimal control,

• Application of mathematical programming, singular perturbations, practical

examples.

References:

1. Enid R. Pinch, ”Optimal Control and Calculus of variation”, Oxford University Press.

2. D.E.Kirk, ”Optimal Control Theory”, Prentice-Hall, 1970.

3. A.P.Sage and C.C.White II, ”Optimum Systems Control”, 2nd Ed., Prentice-Hall, 1977.

4. D.Tabak and B.C.Kuo, ”Optimal Control by Mathematical Programming”, Prentice

Hall, 1971.

5. B.D.O. Anderson and J.B.Moore, ”Linear Optimal Control”, Prentice-Hall, 1971.

6. F.L. Lewis , V.L. Symmos, ”Optimal Control”, Second Edition, John Wiley, 1995.

(PCC) Sliding Mode Control Teaching Scheme Lectures: 3 hrs/week Tuorial : 1 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1 Design and analyze sliding mode controller for uncertain systems

2 Demonstrate capability to design estimators for state and uncertainty

estimations.

3 Design and analyze discrete sliding mode controller.

Syllabus Contents:

• Notion of variable structure systems and sliding mode control,

• Design continuous sliding mode control, chattering issue,

• Discrete sliding mode control, sliding mode observer, uncertainty

estimation using sliding mode, D iscrete output feedback SMC using

multirate sampling.

• Introduction to higher order sliding mode control, twisting and super

twisting algorithms.

References:

1. Spurgeaon and Edwards, ”Sliding Mode Control Theory and Applications”.

2. B. Bandyopadhyay and S. Janardhanan , ”Discrete-time Sliding Mode Control : A

Multirate-Output Feedback Approach”, Ser. Lecture Notes in Control and Information

Sciences, Vol. 323, Springer-Verlag, Oct. 2005.

3. Yuri Shtessel , Christopher Edwards, Leonid Fridman ,Arie Levant “Sliding Mode Control and Observation “Birkhauser

4. S. Kurode, B. Bandyopadhyay and P.S. Gandhi, ”Output feedback Control for Slosh

free Motion using Sliding modes”, Lambert Publications 2012.

(LC) PG Hardware Laboratory Teaching Scheme Practicals: 6 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Demonstrate Hardware Interfacing with microcontroller

2. Demonstrate use of embedded tools for implementing simple controller.

Syllabus Contents:

This lab includes experiments on study of interrupts, timer, I/O operations, ADC interfacing, programming of microcontroller and DSP’s, interfacing with LED display (single / 7 segment) / relay, SPWM generation, control of electric motors, implementation of DFT/FFT algorithms, FIR and IIR filters and other relevant advanced applications.

References:

(LLC) Liberal Learning Course Teaching Scheme Lectures: 0


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Demonstrate the additional information related to the area of their interest

may not be even technical with enthusiasm.

2. Demonstrate their hidden talent in the area of their interest.

Syllabus Contents: It’s a liberal learning..

(MLC) Intellectual Property Rights

Teaching Scheme Lectures: 3hrs/week


Course Outcomes: At the end of the course, students will demonstrate the ability to:

1. Understand that today’s world is controlled by Computer, Information Technology,

but tomorrow world will be ruled by ideas, concept, and creativity.

2. Understand that when IPR would take such important place in growth of

individuals & nation, it is needless to emphasis the need of information about

Intellectual Property Right to be promoted among students in general &

engineering in particular.

3. Understand that IPR protection provides an incentive to inventors for further

research work and investment in R & D, which leads to creation of new and better

products, and in turn brings about, economic growth and social benefits.

Syllabus Contents:

Introduction: Nature of Intellectual Property: Patents, Designs, Trademarks and Copyright. Process, Patenting and Development: technological research, innovation, patenting, development. International Scenario: International cooperation on Intellectual Property. Procedure for grants of patents, Patenting under PCT.

Patent Rights: Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical Indications

New Developments in IPR: Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Softwares etc. Traditional knowledge Case Studies, IPR and

References:

1. Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007 2. Mayall , “Industrial Design”, Mc Graw Hill 3. Niebel , “Product Design”, Mc Graw Hill 4. Asimov , “Introduction to Design”, Prentice Hall 5. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “ Intellectual Property in New Technological Age”. 6. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand.

ELECTIVE I

(DEL1) Intelligent Control Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Understand the characteristics, uses and limitations of classical and modern

intelligent control.

2. Gain an understanding of the functional operation of a variety of intelligent

controls, their bio-foundations and modern heuristic optimization techniques.

3. Design and apply simple soft computing and intelligent control methods using

MATLAB-SIMULINK and toolboxes.

Syllabus Contents:

• Intelligent systems, control and intelligent systems Fuzzy and expert control

(standard, Takagi-Sugeno, mathematical chararacterizations, design example),

planning systems (autonomous vehicle guidance for obstacle avoidance,

model predictive control), attentional systems (attentional strategies for

predators/prey),

• Learning and function approximation (function approximation problem),

adaptive control introduction, learning/adaptation (training neural networks

and fuzzy systems with least squares and gradient methods), stable

fuzzy/neural adaptive control,

• evolutionary methods (genetic algorithm, evolutionary design), foraging,

bacteria and connections to optimization and control, foraging, bees and

connections to optimization, swarm stability (cohesion, foraging) competitive

foraging games, coordinated vehicular guidance applications.

References:

1. K. Passino, ”Biomimicry for Optimization, Control and Automation”, springer

verlag,2005.

2. Kevin M. Passino and Stephen Yurkovich, ”Fuzzy Control”, Addison Wesley Longman,

Menlo park,CA 1998.

3. Antsaklis P.J., Passino K.M. ,”An Introduction to Intelligent and Autonomous Control”,

Kluwer Piblishers Norwell MA 1993.

4. Timothi J. Ross, ”Fuzzy logic with engineering applications”, Wiley, 1995.

5. Rossiter, J.A.,”Predictive Control: a practical approach”, CRC Press, 2003.

6. Simons Haykins,’Neural Networks’ Prentis Hall.

(DEL1) System Identification and Adaptive Control

Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Understand correlation analysis

2. Identify of linear nonparametric and parametric models.

3. Demonstrate concept of adaptive control, Gain scheduling Control, MRAC,

Direct and Indirect Adaptive Control, Adaptive Pole Placement Control.

Syllabus Contents:

• Review of probability theory and random variables: transformation (function) of

random variables, conditional expectation, development of first principle models

and liberalization, state estimation for linear perturbation models (Luenberger

observer),

• Development of grey box models, discrete time series models: FIR and ARX

models, development of ARX models by least square estimation, unmeasured

disturbance modeling: ARMAX, OE, Box-Jenkins’s models,

• Parameter estimation using prediction error method and instrumental variable

method, maximum likelihood estimation, distribution of bias and variance errors,

input signals, recursive approaches to identification, controller design.

• Introduction to adaptive control, Introduction to adaptive control scheme.

References:

1. Papoulis, ”Probability, Random Variables and stochastic processes”, 2nd Ed., McGraw

Hill, 1983.

2. George E.P.Box, Gwilym M.Jenkin,George C. Reinsel, ”Time series analysis,forcasting

and Control”.

3. L. Ljung, ”System Identification Theory for the user”, Prentice-Hall, 1999.

4. Rik Pintelon, John Schouleens, ”System Identification”, IEEE Press.

5. Young, Peter, ”Recursive Estimation and Time Series Analysis”, Springer Verlag

Berlin, 1984.

6. Soderstrom and Stoica, ”System Identification”, Prentice Hall, 1989.

7. Karl J. Åström, Björn Wittenmark,” Adaptive Control” second edition

(DEL1) Model Predictive Controls Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Describe concept of MPC

2. Design and analyze MPC

Syllabus Contents:

• Review of single input single output (SISO) control; model based control;

multivariable control strategies, model forms for model predictive control,

model forms for model predictive control;

• Predictive control strategy, prediction model, constraint handling prediction

equations, unconstrained optimization, infinite horizon cost incorporating

constraints, quadratic programming,

• Closed-loop properties of model predictive control, incorporating

constraints, quadratic programming, interior point QP algorithms, closed-

loop properties of model predictive control, coping with uncertainty, MPC

with integral action, robustness to constant disturbances, robust constraint

satisfaction.

• Pre-stabilized predictions, analysis of dynamic matrix control (DMC) and

generalized predictive control (GPC) schemes, controller tuning and

robustness issues; extensions to constrained and multivariable cases.

References:

1. L. Ljung, ”System Identification - Theory for the User”, Prentice Hall, 1987.

2. E. Camacho and C. Bordons, ”Model Predictive Control in the Process Industry”, 1995.

3. Rawlings, J.B. and Mayne, ”Model Predictive Control: Theory and Design”, Nob Hill

Publishing, 2009.

4. Maciejowski J.M., ”Predictive control with constraints”, Prentice Hall, 2002.

5. Rossiter, J.A., ”Predictive Control: a practical approach”, CRC Press, 2003

6. Soderstrom and Stoica, ”System Identification”, Prentice Hall, 1989

ELECTIVE II

(DEL2) : Fractional Order Modelling and Control



Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. To illustrate concept of fractional calculus

2. To develop fractional order models.

3. To describe fractional control analysis in time domain and frequency domain

4. To design and analyze fractional control strategies.

Syllabus Contents:

• Review of basic definitions of integer-order (IO) derivatives and integrals and their

geometric and physical interpretations, Definition of Riemann-Liouville (RL)

integration, Definitions of RL, Caputo and Grunwald-Letnikov (GL) fractional

derivatives (FDs), Various geometrical and physical interpretations of these FDs,

Computation of these FDs for some basic functions like constant, ramp,

exponential, sine, cosine, etc., Laplace and Fourier transforms of FDs.

• Study of basic functions like Gamma function, Mittag-Leffler function, Dawson’s

function, Hypergeometric function, etc, Analysis of linear fractional-order

differential equations (FDEs): formulation, Solution with different FDs, Initial

conditions, Problem of initialization and the remedies.

• Concepts of ‘memory’ and ‘non-locality’ in real-world and engineering systems,

non-exponential relaxation, ‘Mittag-Leffler’ type decay and rise, Detailed analysis of

fractional-order (FO) modeling of: electrical circuit elements like inductor,

capacitor, electrical machines like transformer, induction motor and transmission

lines, FO modeling of viscoelastic materials, concept of fractional damping, Models

of basic circuits and mechanical systems using FO elements, Concept of anomalous

diffusion, non-Gaussian probability density function and the development of

corresponding FO model, FO models of heat transfer, A brief overview of FO models

of biological systems.

• Review of basic concepts of complex analysis, Concepts of multivalued functions,

branch points, branch cuts, Riemann surface and sheets, Fractional-order transfer

function (FOTF) representation, Concepts like commensurate and non-

commensurate TFs, stability, impulse, step and ramp response, Frequency

response, non-minimum phase systems, Root locus, FO pseudo state-space (PSS)

representation and the associated concepts like solution of PSS model,

controllability, observability, etc.

• Detailed discussion and analysis of superiority of FO control over the conventional

IO control in terms of closed-loop performance, robustness, stability, etc., FO lead-

lag compensators, FO PID control, design of FO state-feedback, Realization and

implementation issues for FO controllers, survey of various realization methods and

the comparative study.

• Primer on MATLAB and Mathematica, Computation of FDs using MATLAB,

Analytical expressions for FDs using Mathematica, Use of Mittag-Leffler functions

and various special functions in MATLAB, Analysis of system of non-linear FDEs

using these softwares, Use of simulink in analysis of FO systems and control.

References:

1. K. B. Oldham and J. Spanier,. The Fractional Calculus. Dover Publications, USA, 2006.

2. Kilbas, H. M. Srivastava, and J. J. Trujillo. Theory and Applications of Fractional Differential Equations. Elsevier, Netherlands, 2006.

3. Podlubny. Fractional Differential Equations. Academic Press, USA, 1999. 4. A. Monje, Y. Q. Chen, B. M. Vinagre, D. Xue, and V. Feliu. Fractional-order Systems

and Control: Fundamentals and Applications. Springer-Verlag London Limited, UK, 2010.

5. R. L. Magin. Fractional Calculus in Bioengineering. Begell House Publishers, USA, 2006.

6. R. Caponetto, G. Dongola, L. Fortuna, and I. Petras. Fractional Order Systems: Modeling and Control Applications. World Scientific, Singapore, 2010.

7. K. S. Miller and B. Ross. An Introduction to the Fractional Calculus and Fractional Differential Equations. John Wiley & Sons, USA, 1993.

8. S. Das. Functional Fractional Calculus for System Identification and Controls. Springer, Germany, 2011.

9. . Ortigueira. Fractional Calculus for Scientists and Engineers. Springer, Germany, 2011.

10. Petras. Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation. Springer, USA, 2011.

11. W. R. LePage. Complex Variables and the Laplace Transform for Engineers. Dover Publications, USA, 2010.

12. H. Ruskeepaa. Mathematica Navigator: Mathematics, Statistics and Graphics. Academic Press, USA, 2009.

(DEL2) Power Electronics Converters: Modeling & Control Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Analyze and model the behavior of converters.

2. Design and control for the desired performance.

Syllabus Contents:

• Switched and averaged models; small/large-signal models; time/frequency models. Analysis of models.

• Linear control approaches normally associated with power converters; resonant controllers

• Nonlinear control methods including feedback linearization, stabilizing, passivity-based, and variable-structure control.

References:

1. Seddik Bacha , Iulian Munteanu , Antoneta Iuliana Bratcu “ Power Electronics

Converters Modeling & Control “ Springer.

2. Keng C. Wu,” Switched Mode Power Converters: design and analysis”, Elseware

academic press

3. K. Kit Sum,” Switch Mode Power Conversion: Basic Theory and Design”,

(DEL2) Control Related Estimations



Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Understand Kalman filters for state estimation 2. Design of estimator

Syllabus Contents:

• Introduction to random variables mean variance, normal distribution, stochastic

estimation, • Introduction to Kalman Filter, Kalman filter elementary approach, linearized and

extended Kalman filter. Unscented kalman filter, particle filter, • Model based estimation of states and disturbance. Robust estimation. Use of

estimation approach for detection and diagnosis.

References:

1. Charles K. Chui, Guanrong Chen,” Kalman Filtering: With Real-Time Applications “, Springer Notes

2. Harold Wayne Sorenson,” Kalman Filtering: Theory and Application”, IEEE Press, 1960 –

(DEL2) Robust Control Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Understand Parametric stability margin

2. Demonstrate generalized stability analysis.

3. Demonstrate control design for robust performance.

Syllabus Contents:

• Introduction to robust control . Introduction to various uncertainties. Stability ball in

a coefficient space. L2 stability ball, robust stability of disc polynomials.

• The parametric stability margin. Interval Polynomials Kharitnov’s Theorem.

Generalized Kharitnov Theorem. Small Gain Theorem,

• Worst case H∞ stability margin. Worst Case Parametric Stability Margin. Robust

Small Gain Theorem, Robust Performance.

• Multilinear Interval Systems: The Mapping Theorem. State Space Parameter Perturbations. Robust Parametric Stabilization.

References:

1. S. P. Bhattacharrya, H. Chapellat and L. H. Keel,”Robust Control the parametric approach” Prentis hall, 1995

2. Michael Green, Canberra, Australia and David J.N. Limebeer,”Linear Robust Control” Pearson Education, Inc

3. Uwe Mackenroth - 2004 - Preview - More editions “Robust Control Systems theory and case studies" Springer

(DEL2) Industrial PID Control



Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Analyze PID controller in industry.

2. Tune of PID controller.

Syllabus Contents:

• Introduction to PID control. Control, structures of PID controllers, Digital

Implementation choice of the controller type, tuning issue. Automatic tuning. The

Significance of the Filter in PID Design Anti-windup Strategies: Integrator Windup,

Avoiding saturation, Combined Approaches. Automatic Reset Implementation

• Set-point Weighting, Constant Set-point Weight Design, variable set-point weighting.

Methodology. Tuning procedure. Linear causal feed forward action . Nonlinear

Causal feed forward action . Non causal feed forward action: Continuous-time Case

Generalities. Methodology.

• Feed forward Action for Disturbance Rejection. PID Control of High-order Systems

Internal Model Control Design . . Process Model Reduction. Controller Reduction .

Performance Assessment: Generalities. Stochastic performance assessment.

Minimum variance control assessment of performance .

• Assessment of PID control performance. Deterministic performance assessment.

Useful functionalities optimal performance for Single-loop Systems Tuning

Assessment. Cascade Control. Relay feedback sequential auto-tuning .

• Tuning of the general cascade control structure. Use of a Smith Predictor in the

Outer Loop. Two Degree-of-freedom control structure , ratio control.

References:

1. Antonio Visioli, ’ Practical PID Control”

2. Karl Johan Åström, Tore Hägglund, ‘ Advanced PID Control’

3. Michael A Johnson, Mohammad H. Moradi ,’ PID Control: New Identification and Design Methods’

Any other course approved by DPPC can be floated

SEMESTER III

(Project) Project Stage I



Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Review literature and formulate problem

2. Analyse problem criticality.

3. Write technical report .

4. Present the work done.

SEMESTER IV

(Project) Project Stage II



Course Outcomes:

At the end of the course, students will demonstrate the ability to: 1. Investigate solution

2. Interpret and analyze findings.

3. Write technical report.

4. Present the work done.

5. Disseminate the findings.

The M. Tech. Project is aimed at training the students to analyze independently any problem in the field of Electrical Engineering or interdisciplinary. The project may be analytical, computational, experimental or a combination of the three. The project report is expected to show clarity of thought and expression, critical appreciation of the existing literature and analytical, experimental, computational aptitude of the student. Progress of the dissertation work will be evaluated in three stages in two semesters.

(DEL2) Teaching Scheme Lectures: 3 hrs/week


Course Outcomes:

At the end of the course, students will demonstrate the ability to:

Syllabus Contents:

m. tech. (electrical) curriculum structure specialization ...m. tech. (electrical) curriculum...

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