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 offline, 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.

Type-2 Fuzzy Logic Control in Computer Games

“…The main goal of the game is to control the bird's height while attempting to fly between the obstacles (i.e. the pipes) without hitting them [11], [33].…”

“…In this version of this game, the game parameters are grouped as the parameters of the environment ( , , , , , ) and the bird's dynamics ( , , ). These parameters are defined as follows with their illustration in Fig.5 [11], [33]:…”

“… Control signal ( ) is the binary input variable ∈ {0,1} provided by the user to flap the bird on the y direction velocity ( ) [11].…”

“…Furthermore, collected behavior logs of the human players from their plays might also be a source for social scientists [8]. Consequently, several games have been used as test beds such as Pacman [5], Scrabble [6], Super Mario [9], Counter-Strike [2], StarCraft [10], Flappy Bird [11], [12] and, Lunar Lander [13], [14].…”

“…In this chapter, we will present the novel applications of the IT2-FLCs into the research area of computer games. In this context, we will handle two well-known computer games, namely Flappy Bird [11], [12] and Lunar Lander [13], [14], to show the abilities of the IT2-FLCs. From a control engineering point of view, as the game player can be seen as the main controller in a feedback loop, we will transform the game logic of flappy bird into a reference tracking problem while handling the moon landing problem as a position control problem.…”