Syllabus for credit based curriculum 2010 – 2011 onwards

CL 677 ADAPTIVE CONTROL

System Identification: Introduction, dynamic systems, models, system identification procedure. Simulation and Prediction. Non-parametric time and frequency domain methods.

Linear dynamic system Identification: Overview, excitation signals, general model structure, time series models, models with output feedback, models without output feedback. Convergence and consistency.
Parameter estimation methods, minimizing prediction errors, linear regressions and Least squares method, Instrumental – variable method, prediction error method. Recursive algorithms. Closed-loop Identification.
Adaptive Control: Close loop and open loop adaptive control. Self-tuning controller. Auto tuning for PID controllers: Relay feedback, pattern recognition, correlation technique.
Adaptive Smith predictor control: Auto-tuning and self-tuning Smith predictor. Adaptive advanced control: Pole placement control, minimum variance control, generalized predictive control.
TEXT BOOKS:

1. 
   Ljung .L, System Identification: Theory for the user, Prentice Hall, Englewood Cliffs, 1987.
   
2. 
   Astrom .K, Adaptive Control, Second Edition, Pearson Education Asia Pte Ltd, 2002.
   

REFERENCES:

1. 
   Chang C. Hong, Tong H. Lee and Weng K. Ho, Adaptive Control, ISA press, Research Triangle Park, 1993.
   
2. 
   Nelles. O, Nonlinear System Identification, Springer Verlag, Berlin, 2001.
   

CL 678 MICROELECTRO-MECHANICAL SYSTEMS

Introduction, emergence, devices and application, scaling issues, materials for MEMS, Thin film deposition, lithography and etching.

Bulk micro machining: Introduction, etch-stop techniques, dry etching, buried oxide process, silicon fusion bonding, and anodic bonding.
Surface micro machining: Introduction, sacrificial layer technology, material systems in sacrificial layer technology, plasma etching, combined IC technology and anisotropic wet etching.

Microstereolithography: Introduction, Scanning Method, Projection Method, Applications. LIGA Process: Introduction, Basic Process and Application

MEMS devices, electronic interfaces, design, simulation and layout of MEMS devices using CAD tools.
TEXT BOOKS:

1. 
   S.M. Sze, Semiconductor Sensors, John Wiley & Sons, INC., 1994.
   
2. 
   M.Elwenspoek, R.Wiegerink, Mechanical Microsensors, Springer-Verlag Berlin Heidelberg, 2001.
   

REFERENCES:

1. 
   Massood Tabib-Azar, Microactuators - Electrical, Magnetic, Thermal, Optical, Mechanical, Chemical and Smart structures, Kluwer Academic Publishers, New York, 1997.
   
2. 
   Eric Udd, Fiber Optic Smart Structures, John Wiley & Sons, New York, 1995.
   

CL 679 ADVANCED APPLIED PROCESS CONTROL
Control relevant process modeling and identification: Model applications, types of models, empirical dynamic models, model structure considerations, model identification.
Identification examples: SISO furnace parametric model identification, MISO parametric model identification, MISO non-parametric identification of a non-integrating process, MIMO identification of an integrating and non-integrating process, design of plant experiments, conversion of model structures.
Linear multivariable control: Interaction in multivariable systems, Dynamic matrix control, properties of commercial MPC packages.
Multivariable optimal constraint control algorithm: Model formulation for systems with dead time, model formulation for multivariable processes with and without time delays, model formulation in case of a limited control horizon, Non-linear transformations.
Nonlinear multivariable control: Non-linear model predictive control, non-linear quadratic DMC, generic model control, GMC application to chemical engineering systems, one step reference trajectory control.
TEXT BOOKS/REFERENCES:

1. 
   B. Roffel, B.H.L. Betlem, “Advanced Practical Process Control” Springer, 2004.
   
2. 
   Jean Pierre Corriou, “Process Control: Theory and applications” Springer, 2004.
   
3. 
   C.A. Smith and A.B. Corrupio," Principles and Practice of Automotive Process Control", John Wiley, New York, 1976
   

CL680 PIPING AND INSTRUMENTATION
P&I Diagram objectives. Industry Codes and Standard. Government regulations
Engineering fluid diagrams. Electrical diagrams. Electronic diagrams. Logic diagrams.
DCS diagrams. Construction diagrams.
Format. Equipment. Instrumentation and Controls.
Application of P&I diagrams in HAZOPS and Risk analysis
Laboratory: Students are required to produce P&I Diagrams using software packages during the laboratory period of the course.
References:

1. 
   Industry Codes and Standards
   
2. 
   American National Standards Institute (ANSI)
   
   1. 
      ANSI/FCI 70-2-2003 – Control Valve Seat Leakage
      
3. 
   American Society of Mechanical Engineers (ASME)
   
   1. 
      ASME Boiler and Pressure Vessel Code. Section VIII – Pressure Vessels
      
4. 
   The Instrumentation, Systems, and Automation Society (ISA)
   
   1. 
      ISA 5.1 – Instrumentation Symbols and Identification
      
   2. 
      ISA 5.2 – Binary Logic Diagrams for Process Operations
      
   3. 
      ISA 5.3 – Graphic Symbols for Distributed Control / Shared Display
      
5. 
   Instrumentation, Logic and Computer Systems
   
   1. 
      ISA 84.01 – Application of Safety Instrumented Systems for the Process
      

Industries

6. 
   Tubular Exchanger Manufacturers Association (TEMA)
   
   1. 
      TEMA Standards
      
7. 
   Government Regulations
   
8. 
   Occupational Safety and Health Administration (OSHA)
   
   1. 
      OSHA 29 CFR 1910.119 – Occupational Safety and Health Standards, Process Safety Management of Highly Hazardous Chemicals
      

CL681 MODELING WITH DATA
DISTRIBUTIONS FOR DESCRIPTIONS

Moments, Sample distributions, Non-Parametric description.

LINEAR PROJECTIONS

Principal Component Analysis, Ordinary Least Squares and Generalized Least Squares, Discrete Variables, Multilevel Modelling.

HYPOTHESIS TESTING WITH CENTRAL LIMIT THEOREM

The Central Limit Theorem, The Gaussian family, Testing a Hypothesis, Analysis of Variance, Regression, Goodness of fit.

MAXIMUM LIKELIHOOD ESTIMATION

Log likelihood and related topics, Maximum Likelihood Estimators, Missing Data, Testing with Likelihoods.

MONTE CARLO

Random number generation, Statistics for a Distribution, Statistics for a Parameter, Drawing a Distribution, Non-Parametric Testing.

TEXTBOOK:

Klemens, B., Modelling with Data, Princeton Univ. Press., 2009. (Cheaper Indian Edition is available from Universities Press)

CL683 PROBABILITY AND COMPUTING

Events and Probability: Verifying Polynomial Identities, Verifying Matrix Multiplication, A Randomized min-cut Algorithm.

Discrete Random Variables and Expectations: The Bernoulli and Binomial Random variables, Conditional Expectation, The Geometric Distribution, The Expected Run-time of Quick-Sort.
Moments and Deviations: Markov’s Inequality, Variance and Moments of a Random Variable, Chebyshev’s Inequality, A Randomized Algorithm for Computing the Median.

Chernoff Bounds: Moment Generating Functions, Deriving and Applying Chernoff Bounds, Better Bounds for Special cases.

Balls, Bins and Random Graphs: Poisson Distribution, Poisson Approximation, Hashing, Random Graphs

The Probabilistic Method: Basic Counting Argument, Expectation Argument, De randomization using Conditional Expectations, Sample and Modify, Second Moment Method, The Conditional Expectation Inequality, Lovasz Local Lemma.

Markov Chains and Random walks: Definition and Representations, Classification of States, Stationary Distributions, Random walks on undirected Graphs.

Continuous Distributions and The Poisson Process: Continuous Random variables, Uniform Distribution, Exponential Distribution, Poisson Process, Continuous Time Markov Processes, Markovian Queues.

Entropy, Randomness and Information: The Entropy Function, Entropy and Binomial Coefficients, A measure of Randomness, Compression

The Monte Carlo Method, The DNF Counting Problem, From Approximate Sampling to Approximate Counting, The Markov Chain Monte Carlo Method.

TEXTBOOK:
Mitzenmacher M, & Upfal E., Probability and Computing, Cambridge Univ. Press,2005. (Cheaper Indian Edition is available)

CL685 COMPUTATIONAL TECHNIQUES IN CONTROL ENGINEERING

Review of Linear Algebra – Vector spaces, Orthogonality, Matrices, Vector and Matrix Norms, Kronecker Product

Numerical Linear Algebra – Floating point numbers and errors in computations, Conditioning, Efficiency, Stability, and Accuracy, LU Factorization, Numerical solution of the Linear system Ax = b, QR factorization, Orthogonal projections, Least Squares problem, Singular Value Decomposition, Canonical forms obtained via orthogonal transformations.
Control Systems Analysis – Linear State-space models and solutions of the state equations, Controllability, Observability, Stability, Inertia, and Robust Stability, Numerical solutions and conditioning of Lyapunov and Sylvester equations.
Control Systems Design – Feedback stabilization, Eigenvalue assignment, Optimal Control, Quadratic optimization problems, Algebraic Riccati equations, Numerical methods and conditioning, State estimation and Kalman filter.
Large scale Matrix computations, Some Selected Software – MATLAB, MATHEMATICA, SCILAB.
TEXTBOOKS/REFERENCES/RESOURCES:

1. 
   B.N. Datta, Numerical Methods for Linear Control Systems, Academic Press/Elsevier, 2005 (Low cost Indian edition available including CD ROM).
   
2. 
   G.H. Golub & C.F. Van Loan, Matrix Computations, 4/e, John Hopkins University Press, 2007 (Low cost Indian edition available from Hindustan Book Agency)
   
3. 
   A. Quarteroni, F. Saleri, Scientific Computing with MATLAB, Springer Verlag, 2003.
   
4. 
   www.scilab.org/download/