Pinned Repositories
ADAM
ADAM implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
Autonomous-Racing-LPV-MPP-MPC
Planning and control for autonomous racing vehicles
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
Constraint_RL_MPC
Safe control of unknown dynamic systems with reinforcement learning and model predictive control
Control-and-AI-Algorithms
Implementations of Control (PID, LQ, MPC, ...) and AI (fuzzy logic, Q-learner, SARSA, ...) algorithms
da_rnn
RNN based on Chandler Zuo's implementation of the paper: A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Gaussian-Process-based-Model-Predictive-Control
Project for the course "Statistical Learning and Stochastic Control" at University of Stuttgart
Learning-Model-Predictive-Control-Using-Dynamic-Bicycle-Model-with-RBF-Based-Gaussian-Process
C-AI-S's Repositories
C-AI-S/Learning-Model-Predictive-Control-Using-Dynamic-Bicycle-Model-with-RBF-Based-Gaussian-Process
C-AI-S/ADAM
ADAM implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
C-AI-S/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.
C-AI-S/CasADi-tutorial-examples
Bsed on the CasADi original paper: "CasADi: a software framework for nonlinear optimization and optimal control"
C-AI-S/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
C-AI-S/da_rnn
RNN based on Chandler Zuo's implementation of the paper: A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
C-AI-S/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
C-AI-S/Deep-RL-Policy-Search-for-MPC
This repo is related to UAV Confrontation using Heirarchial MultiAgent Reinforcement Learning
C-AI-S/differentiable-mpc
C-AI-S/efficient-kan
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
C-AI-S/ekf-nn-training
Implement backpropagation and extended kalman filter to train feedforward neural networks.
C-AI-S/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'.
C-AI-S/high_mpc
Policy Search for Model Predictive Control with Application to Agile Drone Flight
C-AI-S/hilo-mpc
HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems
C-AI-S/industrial_nnmpc_2021
Code for the paper, "Industrial, large-scale model predictive control with structured neural networks."
C-AI-S/l2race
Learning to race challenge for 2020 workshop
C-AI-S/l4casadi
Use PyTorch Models with CasADi and Acados
C-AI-S/learning-based-mpc
learning-based model predictive control of autonomous driving
C-AI-S/LSTM-MPC
C-AI-S/ml-casadi
Use PyTorch Models with CasADi and Acados
C-AI-S/mod_vehicle_dynamics_control
TUM Roborace Team Software Stack - Path tracking control, velocity control, curvature control and state estimation.
C-AI-S/Online-KNODE-MPC
C-AI-S/online_vampnets
Online learning version of VAMPnets
C-AI-S/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.
C-AI-S/PythonLinearNonlinearControl
PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
C-AI-S/pytorch_mppi
Model Predictive Path Integral (MPPI) with approximate dynamics implemented in pytorch
C-AI-S/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.
C-AI-S/safe-control-gym
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
C-AI-S/Spacecraft-Anonamly-Detection
C-AI-S/torchphysics