Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

Header-only C++ library for robotics, control, and path planning algorithms. Work in progress, contributions are welcome!

在ros_gazebo中搭建仿真环境，使用purepursuit算法对样条曲线轨迹进行了路径跟踪、使用lqr算法对生成的五次多项式轨迹进行横向路径跟踪。

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

25 path-tracking algorithms are (goint to be) implemented with python.

Dynamics, MPC, LQR, etc. One repo to rule them all.

Optimal Control Strategies on Cart-pole System in Simscape Multibody Simulation

Differential Dynamic Programming controller operating in OpenAI Gym environment.

Master's Thesis Project: Design, Development, Modelling and Simulating of a Y6 Multi-Rotor UAV, Imlementing Control Schemes such as Proportional Integral Derivative Control, Linear Quadratic Gaussian Control and Model Predictive Control on a BeagleBone Blue

Iterative LQG for a couple of MuJoCo models

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

This repository contains different aspects of autonomous mobile robots including motion, control, and estimation. PID, LQR, and MPC controllers for differential drive robot are developed with ROS2. In addition, some filters are covered such as particle filter and ekf for localization.

A toolbox for trajectory optimization of dynamical systems

Repository of implementation of few algorithms for Underactuated Systems in Robotics and solutions to some interesting problems

Modelling and control of a railway vehicle active suspension system to improve the reduction in vibrations in the vehicle.

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

基于神经网络的一阶倒立摆控制

LQR controller for quadrotors

This repository contains CartPole simulator with its GUI, implemented controller (LQR) and generator of random desired position trace. It also contains files to train and test RNN predicting future states of a CartPole.

Various Control Barrier Functions realized on cartpole.