Learning-model predictive control with multi-step prediction for vehicle motion control

Patent metadata

Field	Value
Publication number	US-12296839-B2
Application number	US-202218060023-A
Country	US
Kind code	B2
Filing date	Nov 30, 2022
Priority date	Nov 30, 2022
Publication date	May 13, 2025
Grant date	May 13, 2025

Abstract

A system for learning-model predictive control (LMPC) with multi-step prediction for motion control of a vehicle includes sensors and actuators. One or more control modules each having a processor, a memory, and input/output (I/O) ports are in communication with the sensors and actuators, the processor executing program code portions stored in the memory. The program code portions cause the sensors and actuators to obtain vehicle state information, receive a driver input, and generate a desired dynamic output based on the driver input and the vehicle state information. A program code portion estimates actions of the actuators based on the vehicle state information and the driver input, and utilizes the vehicle state information, the driver input, and the estimated actions of the actuators to select one or more models of a physics-based vehicle model and a machine-learning model of the vehicle to selectively adjust commands to the actuators.

First claim

What is claimed is:

1. A system for learning-model predictive control (LMPC) with multi-step prediction for motion control of a vehicle, the system comprising:
one or more sensors disposed on the vehicle, the one or more of sensors measuring real-time static and dynamic data about the vehicle;
one or more actuators disposed on the vehicle, the one or more actuators altering static and dynamic characteristics of the vehicle;
one or more control modules each having a processor, a memory, and input/output (I/O) ports in communication with the one or more sensors and the one or more actuators, the processor executing program code portions stored in the memory, the program code portions comprising:
a first program code portion that causes the one or more sensors and the one or more actuators to obtain vehicle state information;
a second program code portion that receives a driver input and generates a desired dynamic output based on the driver input and the vehicle state information;
a third program code portion that estimates actions of the one or more actuators based on the vehicle state information and the driver input; and
a fourth program code portion that utilizes the vehicle state information, the driver input, and the estimated actions of the one or more actuators to select one or more models of a physics-based vehicle model and a machine-learning model of the vehicle to selectively adjust commands to the one or more actuators,
wherein the fourth program code portion further receives the vehicle state information, the driver inputs, and the estimated actions of the one or more actuators within the LMPC, wherein the LMPC includes program code for an offline training application and a real-time application, wherein the offline training application further comprises program code that, upon receiving data from the one or more sensors and from the one or more actuators:
generates a dataset;
evaluates each data point of a plurality of data points in the dataset for similarity to other data points in the dataset;
removes repeated data from the dataset; and
upon determining that a new data point in the dataset is within a predefined Euclidean distance of a previous data point in the dataset, removes the previous data point and retains the new data point in the dataset, and upon determining that the new data point in the dataset is not within the predefined Euclidean distance of the previous data point in the dataset, retains the new data point in the data set; wherein each data point in the dataset corresponds to a distinct vehicle dynamic state, and wherein
the real-time application further comprises:
an online machine learning process that predicts actuator outputs for current vehicle state information based on accumulated data from real-time driving, wherein predicted actuator outputs are made according to a mean value and variance corresponding to a squared exponential kernel function.

2. The system of claim 1 wherein the one or more sensors further comprise at least one of:
inertial measurement units (IMUs) capable of measuring orientation in three dimensions, acceleration, and velocity;
Semi Active Damping Suspension (SADS) sensors capable of measuring orientation, position, velocity, acceleration in both linear and rotational aspects;
global positioning system (GPS) sensors capable of measuring a physical location of the vehicle;
wheel speed sensors;
throttle position sensors;
accelerator position sensors;
steering position sensors; and
tire pressure monitoring sensors.

3. The system of claim 1 wherein the second program code portion receives one or more of:
a torque request from the driver; and
a steering input from the driver,
wherein based on measurements from the one or more sensors and an estimation of capacities of the one or more actuators, the torque request and the steering input from the driver are converted into a desired dynamic output that approximates vehicle dynamics that the driver input indicates.

4. The system of claim 1 wherein the real-time application further comprises program code that evaluates reliability of predicted actuator outputs based on a quantity of data points within a predefined Euclidean distance of the predicted actuator outputs and a credible interval of the predicted actuator outputs.

5. The system of claim 4 wherein upon determining that online machine learning predicted actuator outputs are reliable, the system utilizes the machine learning model to correct predictions generated through the physics-based vehicle model of the vehicle.

6. The system of claim 5 wherein upon determining that the online machine learning predicted actuator outputs are less reliable than actuator outputs predicted by the physics-based vehicle model, only predictions from the physics-based vehicle model are used.

7. The system of claim 1 wherein physics-based vehicle model predictions are selectively corrected through application of a learning term Oracle
and minimizing a cost function
such that when the learning term Oracle increases accuracy of the physics-based vehicle model predictions, the learning term Oracle is used to correct the physics-based vehicle model, and when the learning term Oracle does not alter or decreases the accuracy of the physics-based vehicle model predictions, only the predictions of the physics-based vehicle model are used.

8. A method for learning-model predictive control (LMPC) with multi-step prediction for motion control of a vehicle, the method comprising:
measuring, by one or more sensors disposed on the vehicle, real-time static and dynamic data about the vehicle;
altering static and dynamic characteristics of the vehicle with one or more actuators disposed on the vehicle;
utilizing one or more control modules each having a processor, a memory, and input/output (I/O) ports in communication with the one or more sensors and the one or more actuators, the processor executing program code portions stored in the memory, the program code portions comprising:
obtaining vehicle state information from the one or more sensors and the one or more actuators;
receiving a driver input and generates a desired dynamic output based on the driver input and the vehicle state information;
estimating actions of the one or more actuators based on the vehicle state information and the driver input; and
utilizing the vehicle state information, the driver input, and the estimated actions of the one or more actuators to select one or more models of a physics-based vehicle model and a machine-learning model of the vehicle to selectively adjust commands to the one or more actuators;
receiving the vehicle state information, the driver inputs, and the estimated actions of the one or more actuators within the LMPC; and
executing program code for an offline training application, including:
generating a dataset;
evaluating each data point of a plurality of data points in the dataset for similarity to other data points in the dataset;
removing repeated data from the dataset; and
upon determining that a new data point in the dataset is within a predefined Euclidean distance of a previous data point in the dataset, removing the previous data point and retains the new data point in the dataset, and upon determining that the new data point in the dataset is not within the predefined Euclidean distance of the previous data point in the dataset, retaining the new data point in the data set; wherein each data point in the dataset corresponds to a distinct vehicle dynamic state; and

and executing a real-time application within the LMPC, including:
predicting actuator outputs, with an online machine learning process, for current vehicle state information based on accumulated data from real-t

Assignees

• Gm Global Tech Operations Llc
• Univ Waterloo

Inventors

• Khajepour Amir
• Yu Chao
• Zhang Yubiao
• Zhao Qingrong
• Kasaiezadeh Mahabadi SeyedAlireza

Classifications

• B60W50/0097

  Predicting future conditions · CPC title

• B60W40/10

  related to vehicle motion · CPC title

• B60W2556/50

  of positioning data, e.g. GPS [Global Positioning System] data · CPC title

• B60W2510/0604

  Throttle position · CPC title

• B60W2540/10

  Accelerator pedal position · CPC title