Course10110866151 lectures
Drone Systems and Control
Prof. Suresh Sundaram and Prof. Rudrashis Majumder · IISc Bangalore
About
This course provides a comprehensive introduction to drone technology, encompassing fundamentalaerodynamics, simulation, sensor integration, control systems, and autonomous navigation. Beginning withdrone types and safety regulations, the course progresses through 6-DoF modeling in Simulink, sensorcalibration and Kalman filtering in MATLAB, and classical autopilot design. Advanced topics include pathplanning algorithms (RRT, A*), obstacle avoidance techniques (potential fields, collision cones, controlbarrier functions), trajectory tracking, and an introduction to simultaneous localization and mapping(SLAM) and Sim2Real transfer using ROS and AirSim.
Intended audience: Undergraduate and graduate students. Drone industries. Anyone interested in drone navigation, guidance,and control.
Prerequisites: Familiarity with mathematics subjects such as linearalgebra, differential equation, etc.Proficiency in Simulink/MatLab
Lectures
Week 1 Introduction and Mathematical Preliminaries
- Introduction to the course
- Basics of drone operations
- Tutorial 1: Vectors and coordinates
- Tutorial 2: Vectors and coordinate transformations
- Coordinate frames and transformations
- Rigid body transformations
Week 2 Multirotor dynamics and Drone Sensors
- Dynamic model of multirotors
- Drone sensors
- Drone sensors - Context
- MATLAB demonstration - drone sensors
- Basics of Estimation
Week 3 Estimation and Kalman filtering
- Kalman filtering Technique
- Extended Kalman Filters (EKF)
- Matlab Demonstration of Kalman Filtering
- Matlab Demonstration of EKF
Week 4 Introduction to Control System
- Introduction to Control Systems
- Laplace Transforms
- Matlab Demonstration of Laplace Transforms
- Transfer Function Representation
- Matlab Demonstration of Transfer Functions
Week 5 Transient Response and State Space Representations of Systems
- Transient Response
- Matlab Demonstration of Transient Response
- Eigenvalues and Eigenvectors
- State Space Representations
- Matlab Demonstration of State Space Representations
Week 6 Control with State Space and PID Control
- Canonical Forms, State Transition Matrix (STM), Controllability, Observability
- Examples on STM, Controllability and Observability
- Pole Placement Methods
- PID Controller
- Model Predictive Control (MPC)
Week 7 Drone Autopilot Design
- Drone Autopilot Design
- Drone Autopilot Design
- Drone Autopilot Design
- MATLAB Demonstration of Autopilot Design
Week 8 Global Path Planning and Introduction to Sense-And-Avoidance
- Global Path Planning
- A* Algorithm for Global Path Planning
- RRT & RRT* Algorithms Global Path Planning
- Introduction to Sense-and-Avoidance (SaA)
- Sensor for SAA, Trajectory Prediction, Decision making
Week 9 Obstacle Avoidance and Trajectory Tracking of Drones
- Artificial Potential Field (APF)
- Control Barrier Function (CBF)
- Trajectory tracking with PID Control
Week 10 Scenario generation and Autonomous Drones
- Scenario Generation for UAV Operations
- Scenario Generation with MATLAB Demonstrations
- Autonomous Drone
Week 11 Drone Autonomy with Machine Learning
- Essential of Neural Network for Aerial Perception
- Recurrent Neural Network (RNN) for Trajectory Prediction
- Memory Neural Network (MNN) for Trajectory Prediction
Week 12 Group Autonomy and Advanced Drones
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IISc Bangalore
- Department
- Aerospace Engineering
- Taught by
- Prof. Suresh Sundaram, Prof. Rudrashis Majumder
- Level
- Undergraduate
- Language
- English
- Subjects
- Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed