EE650, Basics of Modern Control Systems, 2026-2027, Sem I

This is a course webpage for Basics of Modern Control Systems, 2026-2027, Sem I

Objective of the course:

The objective of this course is to provide a rigorous introduction to modern control theory using state-space methods for system modeling, analysis, estimation, and controller design. It bridges the gap between classical control techniques and contemporary control methodologies used in research and advanced engineering applications.

Outcomes of the course:

At the end of the course, students will be able to model and analyze linear and nonlinear dynamical systems using state-space methods, and evaluate system properties such as controllability, observability, and stability. They will be able to design modern controllers and state estimators, implement them using computational tools, and apply appropriate control techniques to solve practical engineering problems

Instructor

Abhilash Patel, apatel[at]iitk.ac.in

Teaching Assistants

Name	Roll No	Email
Neeta Yadav	231040605	neetayadav23@iitk.ac.in
Prakhar Bajpai	241040621	prakharb24@iitk.ac.in
Lisa Naik	241040615	lisanaik24@iitk.ac.in

Schedule

Wednesdays and Fridays, 18:15–19:30, at L10

Announcements

Important course-related updates will be posted here with the latest announcement appearing at the top. Please check this page regularly in addition to your IITK email.

[30-July-2026] Welcome to EE650! Course webpage is now active. Please download the First Course Handout (FCH) and read it before the next class.
[31-July-2026] Project Guidelines uploaded. Click here.
[14-Aug-2026] Assigment 1 uploaded. Download from here.

Lectures

Lecture	Topics	Class Discussion	Handout	Reading Recommended
L1/30-07-2026	Course Mechanics and motivation	FCH
L2/07-08-2026	Revisit to Linear Algebra	L2	Note 1	MG 2.1-2.4
L3/12-08-2026	State-space modeling	L3	Note 2	MG 3.2-3.5
L4/14-08-2026	Equilibrium, Linearization, Discrete-form; Project Discussion	L4	Note 2	MG 3.2-3.5
L5/19-08-2026	Data-driven modeling or Machine Learning; Parametric Models: AR, ARX, ARMAX, NARMAX, LIP	L5	Note 3
L6/28-08-2026	Models: NN, PINN; Mimicking SS Structure; Learning: Least Square	L6	Note 3
L7/07-09-2026	Implementing Least Square; BDMR	L7	Note 3
L8/08-09-2026	Implementing Recursive Least Square; BDMR	L8	Note 3
L9/09-09-2026	Non-uniqueness of State-space model, Input-Output Model to State-Space Model, State-Space Model to Input-Output Model: CCF	L9	Note 3
11/09-09-2026	State-Space Model to Input-Output Model: OCF, Modal, Diagonalization	L9	Note 3

MATLAB Codes for Simulation

Simulation of Bouncing Ball, modelled as Hybrid System bouncing_ball.m
Simulation of Pendulum, a classical example of nonlinear system pendulum.m
Synthetic data set 1: BDMR of a HALL mess_bdmr.csv
Synthetic data set 2: Electricity of a HALL electricity_demand.csv
MATLAB Code for data-driven BDMR model: AR Model with LEAST SQUARE , ARX Model with LEAST SQUARE , AR Model with RECURSIVE LEAST SQUARE , ARX Model with RECURSIVE LEAST SQUARE

Additional Readings

Do visit https://controlcartoons.com/ to get a comic exposure to control systems.