Performing web tensioning adjustments in a food packaging system based on reinforcement learning

A food packaging machine comprising:

• a plurality of local sub-systems configured to control web tensioning;
• a memory;
• and
• a processor,
• wherein the memory stores instructions that, when executed by the processor, cause the processor to perform a method comprising:
• receiving one or more local variable values indicating measurements by the food packaging machine of one or more physical parameters for a web tensioning sub-system of the plurality of local sub-systems, wherein the one or more local variable values include measurements relating to one or more of:
• a web tension set point or a current web tensioning system position;
• receiving one or more remote variable values indicating measurements by the food packaging machine of one or more physical parameters for one or remote sub-systems, wherein the one or more remote variable values include measurements relating to one or more of:
• web movement control variables, a jaw motion profile, packaging material characteristics, or a filling status;
• determining one or more control parameter values for the web tensioning sub-system, by processing the remote variable values and the local variable values using a reinforcement learning model and a local control model;
• and
• adjusting one or more control parameters of the web tensioning sub-system in accordance with the determined control parameter values.