Linear System Theory NTNU. Two term projects: Helicopter lab and boat lab

LQR-RRT* method is used for random motion planning of a simple pendulum in it's phase plot

Repo for estimation and control simulations in python

Calculates an optimal LQR controller for a noisy inverted pendulum and simulates the result.

Possibility of a decentralized controller and associated communication savings is investigated for speed control of a string of moving vehicles using sparse linear quadratic regulator (LQR).

Contains simple MPC implementation with neural network learned dynamics.

These are the coding assignments given as a part of the course, Underactuated robotics at CMU.

This is the code for various types of LQR and energy shaping swing up control of a simple pendulum.

Optimized control of QUARC helicopter

Optimal Control and Trajectory Tracking for the Ballbot (Nagarajan et al. 2014)

Design of a Linear Quadratic Regulator balance controller for the Inverted Pendulum. After manually initializing the pendulum in the upright vertical position, the balance controller moves the rotary arm to keep the pendulum in this upright position. Moreover it is capable of balancing itself, even if minor external disturbances are given.

This research contains formulation and implementation of LQR (Linear Quadratic Regulator) Guidance for ship based net recovery of fixed wing UAVs

Preliminary analysis of dynamics and control of the Dual-Steering Bicycle.

This project aims to design a LQR controller to control a car following a trajectory as accurate and fast as possible

LQR control of the rrbot (double inverted pendulum)

Collection of control tests written in python 3

Model-free policy gradient algorithm for LQR