C-AI-S

Learning-Model-Predictive-Control-Using-Dynamic-Bicycle-Model-with-RBF-Based-Gaussian-Process

ADAM

ADAM implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.

Basic-MPC-for-a-dynamic-vehicle-model

This Python script performs a Model Predictive Control (MPC) simulation for vehicle lateral control using the CasADi framework. The main objective of this script is to compute optimal controls for a given vehicle's model while considering several constraints.

CasADi-tutorial-examples

Bsed on the CasADi original paper: "CasADi: a software framework for nonlinear optimization and optimal control"

CasADi_MPC_MHE_Python

This repository is an implementation of the work from Mohamed W. Mehrez. I convert the original code in MATLAB to the Python

da_rnn

RNN based on Chandler Zuo's implementation of the paper: A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

Deep-RL-Policy-Search-for-MPC

This repo is related to UAV Confrontation using Heirarchial MultiAgent Reinforcement Learning

differentiable-mpc

efficient-kan

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

ekf-nn-training

Implement backpropagation and extended kalman filter to train feedforward neural networks.

filterpy

Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

high_mpc

Policy Search for Model Predictive Control with Application to Agile Drone Flight

hilo-mpc

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

industrial_nnmpc_2021

Code for the paper, "Industrial, large-scale model predictive control with structured neural networks."

l2race

Learning to race challenge for 2020 workshop

l4casadi

Use PyTorch Models with CasADi and Acados

learning-based-mpc

learning-based model predictive control of autonomous driving

LSTM-MPC

ml-casadi

Use PyTorch Models with CasADi and Acados

mod_vehicle_dynamics_control

TUM Roborace Team Software Stack - Path tracking control, velocity control, curvature control and state estimation.

Online-KNODE-MPC

online_vampnets

Online learning version of VAMPnets

PINNs-based-MPC

We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.

PythonLinearNonlinearControl

PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.

pytorch_mppi

Model Predictive Path Integral (MPPI) with approximate dynamics implemented in pytorch

qlib

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

safe-control-gym

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

Spacecraft-Anonamly-Detection

torchphysics