SYSTEMS AND METHODS FOR UPDATING THE PARAMETERS OF A MODEL PREDICTIVE CONTROLLER WITH LEARNED OPERATIONAL AND VEHICLE PARAMETERS GENERATED USING SIMULATIONS AND MACHINE LEARNING

A computer-implemented method is disclosed for determining optimal operational parameters for a model predictive controller (MPC) for vehicle control. The method includes training a machine learning model by simulating vehicle operations across a range of operational parameters using an MPC framework. The simulation identifies ranges of parameters that recover the vehicle from unstable states, with the bounds corresponding to the vehicle's performance envelope. The method comprises determining an optimum value for a vehicle parameter based on the simulation output, updating training data accordingly, and revising the vehicle's control system using the optimum value to control the operation of an actuator for a particular maneuver. The system may update the parameter set in response to changing vehicle or environmental conditions and supports both autonomous and human-in-the-loop operation. Also disclosed is a vehicle comprising a processing component configured to implement control inputs based on optimized parameters generated via vehicle simulation.