c-ai-s Goto Github PK

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

data-driven-control

A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.

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

demonstration

Projects_Demonstrations

differentiable-mpc

dqn-for-lane-change-in-highway-scenarios

Master's thesis about Deep Reinforcement Learning for Decision Making in autonomous driving

dualstateandparameterestimation_ekf-ukf-pf

efficient-kan

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

efficienteffectivelstm

Experiment Codes for the paper "An Efficient and Effective Second-Order Training Algorithm For LSTM-based Adaptive Learning"

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'.

gaussian-process-based-model-predictive-control

Project for the course "Statistical Learning and Stochastic Control" at University of Stuttgart

gp-mpc

MPC with Gaussian Process

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

ilqr

CasADi implementation of the iterative linear quadratic regulator

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

lbmpc

learning-based model predictive control

learning-based-mpc

learning-based model predictive control of autonomous driving

learning-model-predictive-control-using-dynamic-bicycle-model-with-rbf-based-gaussian-process