ml4cps.examples.examples module

class ml4cps.examples.examples.BuckConverter

Bases: Automaton

guards(q, x)
time_discrete_dynamics(q, p, x, u)
class ml4cps.examples.examples.TunnelOven

Bases: Automaton

time_discrete_dynamics(q, p, x, u)
ml4cps.examples.examples.buck_converter()

Credits to the FAMOS paper authors.

Returns:

ml4cps.examples.examples.conveyor_system_sfowl(split=False)

Conveyor system of SFOWL.

ml4cps.examples.examples.simple_conveyor()

We model the discrete-event controller with a three-state automaton. The idle conveyor is in state $q_{idle}$ until an item with a mass $M$ and a destination distance $D$ is put on it. Then it switches to $q_{move}$ during which the conveyor is moving the item. It is in this state until the destination position is reached, and it switches to $q_{settle}$. After $T_{settle}$ amount of time it is again in $q_{idle}$. :return:

ml4cps.examples.examples.simple_conveyor_8_states()

Simple conveyor model with 8 discrete states is defined in: [1] N. Hranisavljevic, A. Maier, and O. Niggemann, “Discretization of hybrid CPPS data into timed automaton using restricted Boltzmann machines,” Engineering Applications of Artificial Intelligence, vol. 95, p. 103826, 2020, doi: https://doi.org/10.1016/j.engappai.2020.103826.

Returns:

Automaton object.

ml4cps.examples.examples.tunnel_oven(complexity='111')