Estimation of Signals and Systems Detailed Syllabus for Control Engineering/ Control Systems M.Tech first year first sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.
The detailed syllabus for Estimation of Signals and Systems M.Tech 2017-2018 (R17) first year first sem is as follows.
M.Tech. I Year I Sem.
Course Objectives:
- To expose students to different system identification concepts.
- To make the use of random-process models to represent non-deterministic signals and noise
- To extract the time-domain and frequency-domain structure of the signals and noise from the models
Course Outcomes: Upon the completion of this course, the student will be able to
- Apply the concepts of developing mathematical models for industrial systems,
- Develop models from first principles and data driven models.
UNIT I: Review of Probability theory and random variable – random process – Linear Regression. ARMAX Model Structure.
UNIT II: Concept of estimation, Recursive least squares (RLS), Consistency of estimation, Weighted LS. Convergence analysis LS. Parametric models – LS estimation, bias – Generalized Least Squares (GLS)
UNIT III: Parameters estimation using Instrumental Variable (IV) method. Persistently exciting input signal – Likelihood functions and Maximum Likelihood Estimation (MLE) – Singular Value Decomposition (SVD).
UNIT IV: Kalman filter, State estimation using Kalman filter, Parameter estimation using Kalman filter.
UNIT V: Extended Kalman Filters (EKF), State and Parameter estimations of nonlinear systems using EKF.
TEXT BOOKS:
- Papoulis and Pillai, Probability, Random Variables and Stochastic Process, McGraw Hill, 2002.
- Jerry M. Mendel, Lessons in Estimation Theory for Signal Processing, Communications, and Control, Prentice – Hall, 1995.
REFERENCES:
- Karl J Astrom, Introduction to Stochastic Control Theory, Mathematics in Series and Engg., Vol. 70.
- Michel Verhaegen and Vincent Verdult, Filtering and System Identification A Least Squares Approach, Cambridge Univ. Press, 2007.
- M.S. Grewal and A. P. Andrews, Kalman Filtering Theory and Practice Using Matlab, John Wiley, 2008.
For all other M.Tech 1st Year 1st Sem syllabus go to JNTUH M.Tech Control Engineering/ Control Systems 1st Year 1st Sem Course Structure for (R17) Batch.
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