hilbert521 Goto Github PK

heteroagents

Some codes for heterogeneous agent models

how-to-learn-robotics

Open-source study guide of Robotics

ifac_charging_facility

"Multi-Level Electric Vehicle Charging Facilities with Limited Resources" submitted to IFAC 2020

inertiawheelpendulum

Conrol theory project a friend and I did for our Intelligent Control class during our minor (undergraduate level)

intelligent-control-techniques-for-robots

An analysis of different intelligent control techniques (evolutionary alg., reinf. learn., neural networks) applied to robotic manipulators.

introduction-to-communication

Transmitter and receiver design over acoustic channel

inverted-pendulum-modeling-

Fuzzy logic control

iros2020_learningbarriers

Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach

itvsim

A simple MATLAB simulator for prototyping control strategies on multi-agent aeromagnetic surveys

jmu

Contains some of my collected work from my time at JMU

learning-based-mpc

Learning-Based Model Predictive Control (LBMPC)

lihang-code

《统计学习方法》的代码实现

lihang_book_algorithm

致力于将李航博士《统计学习方法》一书中所有算法实现一遍

lmifilter_1229

lyapunov-optimization

Codes for Lyapunov optimization.

machine-learning-session

madrl

Repo containing code for multi-agent deep reinforcement learning (MADRL).

magnet

MAGNet: Multi-agents control using Graph Neural Networks

manim

Animation engine for explanatory math videos

marl-papers

Paper list of multi-agent reinforcement learning (MARL)

mas

Multi-Agent System

massspringsystem

Code for the paper "Lyapunov Stability Analysis of a Mass-Spring system subject to Friction"

masters-thesis

Model predictive control of micro aerial vehicle using onboard microcontroller

matlabmulti-agentframework

minirover-hardware

ml-road

Machine Learning Resources, Practice and Research

model-estimation-of-non-linear-dynamical-system

The stochastic approach to predict the model estimation is a recently introduced control strategy for the underactuated systems (hopping robot) that allows the system to predict its trajectory movements in the unpredicted environment. In this work, we demonstrate the stochastic approach through Machine Learning technique (Gaussian Mixture Model) which allows us to predict the state information by interpreting with the sample state information of the Hopping robot through a probabilistic approach. The output of this approach will yield us with the desired estimated state of the system. Finally, we are going to hand over our work for applying this estimated policy to the simulated Hopping robot by using an Iterative Linear Quadratic Regulator (iLQR) as a controller which updates the predicted model estimation to the existing control policy of the system to determine its optimal trajectory motion.

mpc-design-tools

VirtualArena package for the design of MPC controllers with closed-loop guarantees.

mpcbenchmarking

Open collection of model predictive control (MPC) benchmarking problems

msn-flocking-formation-control

A MATLAB program that implements Multi-agent flocking, following a target, avoiding obstacles