Aerial Robotics IITK
  • Introduction
  • Danger Zone
  • Tutorials
    • Workspace Setup
      • Installing Ubuntu
      • Basic Linux Setup
      • Spruce up your space
      • ROS Setup
      • PX4 Setup
        • PX4 Toolchain Setup
      • Ardupilot Setup
      • Installing Ground Control Station
        • QGroundControl
        • Mission Planner
      • ArduPilot Setup on Docker
      • PX4 Setup on Docker
    • How to Write a ROS Package
      • ROS Package
      • Node Handles, Parameters, and Topics
      • Coding Standards
      • Custom mavros message
      • Transformations
      • Conversions
    • Cheatsheets
      • CMakeCheatsheet
      • GitCheatsheet
      • LatexCheatsheet
      • Markdown Cheatsheet
    • Miscellaneous
      • Odroid XU4 Setup
      • Simulation using Offboard Control
        • Enable Offboard Mode in PX4
      • Writing a UDev rule
      • Sensor fusion
    • Reference wiki links
  • Concepts
    • Quaternions
      • Theory
    • Kalman Filters
    • Rotations
    • Path Planning
      • Grassfire Algorithm
      • Dijkstra Algorithm
      • A* Algorithm
      • Probabilistic Roadmap
      • RRT Algorithm
      • Visibility Graph Analysis
    • Lectures
      • Aerial Robotics
      • Avionics
      • Control Systems: Introduction
      • Control Systems: Models
      • Inter IIT Tech Meet 2018
      • Kalman Filters
      • Linux and Git
      • Git Tutorial
      • ROS
      • Rotorcraft
      • Software Training
  • Control System
    • Model Predictive Control
      • System Identification
      • Sample SysId Launch Files
      • Running MPC
        • MPC with Rotors
        • MPC with PX4 Sim
        • MPC with ROS
      • References
    • PID Controller
      • Introduction
      • Basic Theory
  • Estimation
    • Visual-Inertial Odometry
      • Hardware Requirements
      • Visual-Inertial Sensing
      • DIYing a VI-Sensor
    • Setup with VICON
    • Odometry from pose data
  • Computer Vision
    • Intel RealSense D435i setup for ROS Noetic
    • IntelRealSense D435i Calibration
    • Camera Calibration
    • ArUco ROS
  • Machine Learning
    • Datasets
  • Hardware Integration
    • Configuring Radio Telemetry
    • Setting up RTK + GPS
    • Integration of Sensors with PixHawk
      • Connecting Lidar-lite through I2C
    • Connections
    • Setting up Offboard Mission
      • Setting up Companion Computer
        • Raspberry Pi 4B Setup
        • Jetson TX2 Setup
      • Communication Setup
      • Guided mode
    • Miscellaneous
  • Resources
    • Open-source algorithms and resources
    • Courses
      • State Space Modelling of a Multirotor
      • Path Planning Lecture
      • Introduction to AI in Robotics
      • RRT, RRT* and RRT*- Path Planning Algorithms
    • Useful Reading Links
      • Aerial Robotics
      • Books
      • Computer Vision and Image Processing
      • Courses on AI and Robotics
      • Deep Neural Network
      • Dynamics and Controls system
      • Motion Planning
      • Probabilistic Robotics
      • Programming
      • Robotics Hardware
      • Miscellaneous and Awesome
    • Online Purchase websites
  • Competitions
    • Inter-IIT TechMeet 8.0
    • Inter-IIT TechMeet 9.0
    • IMAV 2019, Madrid, Spain
    • Inter-IIT TechMeet 10.0
    • Inter-IIT TechMeet 11.0
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Control System

This section describes the controllers used by our robots.

PreviousLecturesNextModel Predictive Control

Last updated 1 year ago

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We have implemented two type of controllers for our MAVs:

  • Model Free controller

    • Proportional Integrator Derivative (PID) controller :

  • Model Based controller

    • Model Predictive Controller (MPC)

PID Controller
Model Predictive Control