Systems and methods for updating the parameters of a model predictive controller with learned controls parameters generated using simulations and machine learning

A vehicle, comprising:

• a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm comprising a model predictive control problem;
• a data store coupled to the processing component, wherein the data store contains an optimal value for an operational parameter for execution of the control algorithm;
• wherein the processing component is configured to adjust the control input based on the optimal value for the operational parameter;
• wherein the optimal value for the operational parameter is received by the processing component by a vehicle simulation system;
• and
• wherein the vehicle simulation system generated the optimal value for the operational parameter by:
• simulating a vehicle operation across a range of one or more operational parameters by solving the model predictive control problem over a prediction horizon;
• determining, by a trained machine learning vehicle performance circuit, the optimal value for the operational parameter based on a result for the simulated vehicle operation;
• and
• re-training the trained machine learning vehicle performance circuit based on one or more updated training sets incorporating the optimal value for the operational parameter.