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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.

mpc-and-mhe-implementation-in-matlab-using-casadi

This is a workshop on implementing model predictive control (MPC) and moving horizon estimation (MHE) on Matlab. The implementation is based on the Casadi Package which is used for numerical optimization. A non-holonomic mobile robot is used as a system for the implementation. The workshop video recording can be found here https://www.youtube.com/playlist?list=PLK8squHT_Uzej3UCUHjtOtm5X7pMFSgAL ... Casadi can be downloaded here https://web.casadi.org/

nominal_nmpc

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

online-knode-mpc

online_vampnets

Online learning version of VAMPnets

optimal-control-via-neural-networks

Code repo for ICLR paper: Optimal Control Via Neural Networks: A Convex Approach

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

racingcarla

Learning Model Predictive Control (LMPC) for autonomous racing in CARLA 3D environment.

safe-control-gym

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