Export of explicit model predictive control to python

Abstract

This paper shows how explicit model predictive control (MPC) strategies can be implemented in Python. They use a pre-calculated map between state measurements and control inputs to simplify and accelerate the calculation of optimal control inputs. By shifting majority of the computational effort off-line, the concept of explicit MPC offers a significantly faster and cheaper implementation of model predictive control. We show how explicit MPC feedbacks are designed and exported to a self-contained Python code that can be easily merged with existing applications. Two examples are provided to illustrate the procedure. One considers the design of an artificial player for a videogame. The second one tackles the problem of quadrocopter control.