US 12,459,497 B2
Systems and methods for updating the parameters of a model predictive controller with learned controls parameters generated using simulations and machine learning
Michael Thompson, Los Altos, CA (US); Carrie Bobier-Tiu, Los Altos, CA (US); Manuel Ahumada, San Jose, CA (US); Arjun Bhargava, San Francisco, CA (US); and Avinash Balachandran, Sunnyvale, CA (US)
Assigned to TOYOTA RESEARCH INSTITUTE, INC., Los Altos, CA (US)
Filed by TOYOTA RESEARCH INSTITUTE, INC., Los Altos, CA (US)
Filed on Feb. 2, 2021, as Appl. No. 17/165,822.
Prior Publication US 2022/0242401 A1, Aug. 4, 2022
Int. Cl. B60W 30/08 (2012.01); B60W 30/02 (2012.01); G05D 1/00 (2024.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC B60W 30/08 (2013.01) [B60W 30/02 (2013.01); G05D 1/0891 (2013.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); B60W 2530/20 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer implemented method for determining and utilizing optimal real time operational parameters to adjust a control input for an actuator of a vehicle comprising a model predictive controller for controlling the vehicle, the method comprising:
receiving, at a hardware processor, from a data store or a graphical user interface, a range for one or more operational parameters associated with the vehicle, the one or more operational parameters comprising one or more controls parameters;
determining, by a trained machine learning vehicle performance circuit, optimum real time values for a controls parameter of the one or more controls parameters, wherein the trained machine learning vehicle performance circuit is trained by:
simulating, by a vehicle control circuit, a vehicle operation across the range of the one or more operational parameters by solving a model predictive control problem over a prediction horizon and across one or more real time steps;
determining, by the trained machine learning vehicle performance circuit, an output of the model predictive control problem, the output including one or more of the optimal real time operational parameters based on a result for the simulated vehicle operation;
updating the trained machine learning vehicle performance circuit based on one or more training sets updated with the optimum real time values for training the trained machine learning vehicle performance circuit; and
re-training the trained machine learning vehicle performance circuit with the one or more updated training sets; and
adjusting the control input for the actuator of the vehicle according to the one or more optimal real time operational parameters to control operation of the actuator.