FPGA-BASED MODEL PREDICTIVE CONTROL

摘要

An FPGA (22) for controlling an electrical converter (12) comprises an enumeration block (32) adapted for generating possible next switch positions (s^new(k)) for semiconductor switches of the electrical converter (12) based on an actual applied switch position (s(k-1)); a plurality of explorer blocks (28), each explorer block (28) adapted for calculating a cost value (J) for a possible next switch position of the semiconductor switches by: receiving a possible next switch position (s^new(k)); calculating system variables at future time instants from system variables at a current time instant of the electrical converter (12) and the load (24) based on the possible next switch position, wherein the system variables at future time instants are calculated from the system variables at the current time instant with differential equations modelling the electrical converter (12) and the load (24); determining a cost value (J) from the system variables at future time instants by evaluating a cost function with the system variables at future time instants; an arbiter block (34) for selecting the next switch position (s(k)) to be applied to the electrical converter (12) from the possible next switch positions by: receiving possible next switch positions (s^new(k)) from the enumeration block (32); selecting a non-operating explorer block (28) and sending a received possible next switch position to the non-operating explorer block; receiving a cost value (J) for the respective possible next switch position from a finished explorer block; when all possible next switch positions received from the enumeration block (32) have been processed, selecting the next switch position (s(k)) as the possible next switch position (s^new(k)) with the lowest cost value (J). Each explorer block (28) is further adapted for determining a prediction horizon (N) for the possible next switch position (snew(k)) at which at least one of the calculated system variables at future time instants has a deviation from a reference for the system variable, which is bigger than a predefined deviation for the system variable. A prediction horizon (N) for a system variable at future time instants is determined via a linear extrapolation, in which the system variable at the future time instants is calculated from the system variable at the current time instant, and the prediction horizon (N) is determined based on an intersection point of the linearly extrapolated system variable between the current time instant and the future time instant with a maximal possible deviation from a reference of the system variable. Moreover, the intersection point is determined iteratively by a binary search.