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

Use PyTorch Models with CasADi for data-driven optimization or learning-based optimal control. Supports Acados.

Reinforcement Learning with Model Predictive Control

A toolkit for testing control and planning algorithm for car racing.

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

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

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 on CPUs, GPUs, and TPUs.

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

An optimization framework that links CasADi, Ipopt, ACADOS and biorbd for Optimal Control Problem

Learning-Based Model Predictive Control (LBMPC)

Implementing trajectory optimization on bipedal system

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 contains the source code for “Unscented Kalman filter stochastic nonlinear model predictive control” (UKF-SNMPC).

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. 🏎️ ✈️

TROPIC: TRajectory OPtimization In CasADi

A basic nonlinear model predictive control implementation using Casadi with Unscented Kalman filter state estimation

This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).

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