EEA | Master of Technology in Control and Automation
Courses id Courses Title
[0 - 0 - 12]
Major Project Part-I
To be decided by the project supervisor.
[3 - 0 - 0]
Linear Systems Theory
Review of matrix algebra, state variable modelling of continuous and discrete time systems, linearization of state equations, solution of state equations of linear time-invariant and timevarying systems, Controllability and observability of dynamical systems, Minimal realization of linear systems and canonical forms, Liapunovs stability theory for linear dynamical systems, State Feedback controllers, Observer and Controller design.
[3 - 0 - 0]
Mathematical Methods in Control
Linear Spaces Vectors and Matrices, Transformations, Norms - Vector and Matrix norms, Matrix factorization, Eigenvalues and Eigenvectors and Applications, Singular Value Decomposition and its Applications, Projections, Least Square Solutions. Probability, Random Variables, Probability distribution and density functions, Joint density and Conditional distribution, Functions of random variables, Moments, characteristic functions, sequence of random variables, Correlation matrices and their properties, Random processes and their properties, Response of Linear systems to stochastic inputs, PSD theorem.
[3 - 0 - 0]
Nonlinear Systems
Introduction to nonlinear systems: Examples of phenomena, models & derivation of system equations. Fundamental properties: Existence & uniqueness, Dependence on initial conditions & parameters. Phase plane analysis. Limit cycles & oscillations. Describing function method and applications. Circle criterion. Lyapunov stability of autonomous systems. Perturbation theory & Averaging. Singular perturbation model and stability analysis. Basic results on Lie algebra. Controllability and Observability of nonlinear systems. Bifurcations. Chaos. Synchronization.
[3 - 0 - 0]
Sensors and Transducers
Transducer Fundamentals: Transducer terminology Design and performance characteristics, --- criteria for transducer selection, Case Studies Transducers principles of representative cases with emphasis on special Electronic Conditioning requirements of different type of sensors-- Resistive transducer; Inductive transducers; capacitive transducers; piezoelectric transducer; semiconductor and other sensing structures. Displacement transducers; tachometers and velocity transducers; accelerometers and gyros; strain gauges; force and torque transducers; flow meters and level sensors; pressure transducers; sound and ultrasonic transducer. Phototubes and photodiodes; photovoltaic and photoconductive cells, photoemission, photo electromagnetic, detectors pressure actuated photoelectric detectors, design and operation of optical detectors, detector characteristics.|Brief Introduction -- Smart Intelligent Sensors, MEMS, Nano.|Transducer Performance: Static and dynamic performance parameters|Standards: Electrical tests, measurement unit, measurement standards of of voltage, current, frequency, impedance etc .|Errors and noise: types of errors, Effect of noise and errors on resolution and threshold. Dynamic range.|Testing: Calibration, dynamic tests, environmental test, life test. |Case Studies in Application of transducers: displacement, velocity, acceleration, force, stress, strain, pressure and temperature measurement. Angular and linear encoders, Radar, laser and sonar distance measurement, Tachometers, Viscometer, densitometer.
[0 - 0 - 24]
Major Project Part-II
To be decided by the project supervisor.
[3 - 0 - 0]
Basic Information Theory
Introduction to entropy, relative entropy, mutual information, fundamental inequalities like Jensens inequality and log sum inequality. Proof of asymptotic equipartition property and its usage in data compression. Study of entropy rates of the stochastic process following Markov chains. Study of data compression: Kraft inequality and optimal source coding. Channel capacity: symmetric channels, channel coding theorem, Fanos inequality, feedback capacity. Differential entropy. The Gaussian channel: bandlimited channels, channels with colored noise, Gaussian channels with feedback. Detailed study of the rate-distortion theory: rate distortion function, strongly typical sequences, computation of channel capacity. Joint source channel coding/separation theorem. There are no laboratory or design activities involved with this course.
[3 - 0 - 0]
Power System Dynamics
Dynamic models of synchronous machines, excitation system, turbines, governors, loads. Modelling of single-machine-infinite bus system. Mathematical modelling of multimachine system. Dynamic and transient stability analysis of single machine and multi-machine systems. Power system stabilizer design for multimachine systems. Dynamic equivalencing. Voltage stability Techniques for the improvement of stability. Direct method of transient stability analysis: Transient energy function approach.
[3 - 0 - 0]
Introduction to Machine Learning
Introduction to Machine intelligence and learning; linear learning models; Artificial Neural Networks: Single Layer Networks, LTUs, Capacity of a Single Layer LTU, Nonlinear Dichotomies, Multilayer Networks, Growth networks, Backpropagation and some variants; Support Vector Machines: Origin, Formulation of the L1 norm SVM, Solution methods (SMO, etc.), L2 norm SVM, Regression, Variants of the SVM; Complexity: Origin, Notion of the VC dimension, Derivation for an LTU, PAC learning, bounds, VC dimension for SVMS, Learning low complexity machines - Structural Risk Minimisation; Unsupervised learning: PCA, KPCA; Clustering: Origin, Exposition with some selected methods; Feature Selection: Origin, Filter and Wrapper methods, State of the art - FCBF, Relief, etc; Semi-supervised learning: introduction; Assignments/Short project on these topics.
[3 - 0 - 0]
Embedded Systems and Applications
Introduction to embedded system. Architectural Issues: CISC, RISC, DSP Architectures.|Component Interfacing, Software for Embedded Systems : Program Design and Optimisation techniques, O.S for Embedded Systems, Real-time Issues. Designing Embedded Systems : Design Issues, Hardware- Software Co-design, Use of UML. Embedded Control Applications, Networked Embedded Systems : Distributed Embedded Architectures, Protocol Design issues, wireless network. Embedded Multimedia and Telecommunication Applications: Digital Camera, Digital TV, Set-top Box, Voice and Video telephony.
[3 - 0 - 0]
Computer Vision
Link between Computer Vision, Computer Graphics, Image Processing and related fields; feature extraction; camera models; multi-view geometry; applications of Computer Vision in day-to-day life.
[3 - 0 - 0]
Adaptive and Learning Control
Introduction to adaptive control, Review of Lyapunov stability theory, Direct and indirect adaptive control, Model reference adaptive control, Parameter convergence, persistence of excitation, Adaptive backstepping, Adaptive control of nonlinear systems, Composite adaptation, Neural Network-based control, Repetitive learning control, Reinforcement learning-based control, Predictive control, Robust adaptive control.
[3 - 0 - 0]
Computational Neuroscience
Fundamentals of brain anatomy and physiology, signals of brain, Brain signal recording and imaging techniques, Human experimentation study design, Processing the X-D neural data, Machine learning approaches, Graph theory and neural networks, Multivariate pattern analysis in 4D Imaging data, Statistical inferences, student projects and presentations.
Block II, IIT Delhi Main Rd, IIT Campus, Hauz Khas, New Delhi, Delhi 110016