A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Reinforcement Learning with Model Predictive Control
#计算机科学#Use PyTorch Models with CasADi for data-driven optimization or learning-based optimal control. Supports Acados.
#学习与技能提升#A toolkit for testing control and planning algorithm for car racing.
adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
This repository is an implementation of the work from Mohamed W. Mehrez. I convert the original code in MATLAB to the Python
Transforms your CasADi functions into batchable JAX-compatible functions. By combining the power of CasADi with the flexibility of JAX, JAXADi enables the creation of efficient code that runs smoothly...
An optimization framework that links CasADi, Ipopt, ACADOS and biorbd for Optimal Control Problem
Learning-Based Model Predictive Control (LBMPC)
RBDL - Rigid Body Dynamics Library - ORB Version - The two main differences to the original rbdl is that this version has error handling and uses polymorphism for constraints
Implementing trajectory optimization on bipedal system
#计算机科学#Deep Neural Network architecture as a predictive optimal controller for {HVAC+Solar cell + battery} disturbance afflicted system vs classic Model Predictive Control
Rigid transform using Lie groups and Dual Quaternions, written in CasADi!
This repository has the code for the nonlinear model predictive controller for target tracking problems with the use of Casadi framework and Matlab simulation environment. 🏎️ ✈️
This repository contains the source code for “Unscented Kalman filter stochastic nonlinear model predictive control” (UKF-SNMPC).
The implement for the Quadrotor trajectory tracking using MPC with CasADi library
TROPIC: TRajectory OPtimization In CasADi
『PythonとCasADiで学ぶモデル予測制御』 サポートサイト
#计算机科学#This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).