Teaching

1) Introduction to UAV Design  (Spring  2021, 2022, 2023, 2024):

Unit 1: Types of UAVs--- Multi-rotors, fixed wing (FWUAV), Hybrid VTOLs.

Unit 2: Multi-rotor design--- Concept of operation (CONOPS), design specifications,  different reference frames, axis conventions, forces and moments, sizing and assembly, sensors and  control.

Unit 3: FWUAV Flight mechanics and control--- wing, fuselage, stabilizer and control surfaces, propulsion system, forces (lift, drag, thrust, side force), moments (roll, pitch, yaw), trim conditions, longitudinal static stability, lateral and directional stability, PID control through successive loop closure.

Unit 4: FWUAV design--- Concept of operation (CONOPS), design specifications, preliminary sizing, airfoil selection, wing planform selection, control surface sizing, stabilizer sizing, selection of propulsion system (battery, motor/engine, propeller), stability and performance analysis, design tradeoffs.

Unit 5: Hybrid VTOL design--- Different configurations (tilt-rotor, tail sitter), transition dynamics, design specifications, sizing, stability and control.

Software used as  a part of the course: XFLR, ROS

2) Digital Systems and Microcontrollers  (Laboratory, Monsoon 2020,  2021, 2022, 2023)

3) Mechatronics System Design (Spring 2022, 2023, 2024)

Unit 1: Sensors and Actuators: 

Sensors: structure of measurement systems, static characteristics, dynamic characteristics.

Sensors for robotics application - position, speed, acceleration, orientation, range.

Actuators - general characteristics, motors, control valves.

Unit 2: Computer based feedback control:

Sampled data control, sampling and hold, PID control implementation, stability, bilinear transformation.

Units 3, 4 and 5  are handled by Dr.  Nagamanikandan Govindan

Software used as  a part of the course:  ROS, FUSION 360  (for units 3, 4  and 5)

4) Topics in Reinforcement Learning (Spring 2023, 2024), Jointly with Dr. Tejas Bodas

Systems with discrete state-action space:  MDPs (handled by Dr. Tejas Bodas)

Systems with continuous state-action space: 

Model-based methods: Controllability, Stability,  State feedback control, Dynamic programming, Value iteration, policy iteration, Linear Quadratic Regulator, Riccati equation.

Model-free methods: Function approximation, actor-critic, integral reinforcement learning, Policy gradient methods.