GRAMPC

Welcome to the GRAMPC ecosystem of nonlinear model predictive control toolboxes.

GRAMPC#

GRAMPC is a nonlinear MPC framework that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. GRAMPC is implemented in plain C with additional interfaces to C++, MATLAB/Simulink, and Python.

Documentation: https://grampc.github.io/grampc/

Github: grampc/grampc

GRAMPC-S#

GRAMPC-S is a framework for nonlinear stochastic model predictive control based on GRAMPC. The implementation in C++ and the fast solver allow sampling times in the (sub)millisecond range.

Documentation: https://grampc.github.io/grampc-s/

Github: grampc/grampc-s

GRAMPC-D#

GRAMPC-D is a framework for nonlinear distributed model predictive control based on GRAMPC. The implementation is in C++ allowing for sampling times in the millisecond range.

Github: grampc/grampc-d

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