APPROACH AND SYSTEM FOR HANDLING CONSTRAINTS FOR MEASURED DISTURBANCES WITH UNCERTAIN PREVIEW

• US 20160216699A1
• Filed: 01/25/2016
• Published: 07/28/2016
• Est. Priority Date: 01/28/2015
• Status: Active Grant

• Current Assignee: Garrett Transportation I Inc. (Garrett Motion, Inc.)
• Original Assignee: Honeywell, Spol. S R.O. (Honeywell International Inc.)
• Inventors: PEKAR, Jaroslav, BERAN, Jaroslav
• Granted Patent: US 10,503,128 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: F02D 29/02 peculiar to engines driving vehicles; peculiar to engines driving variable pitch propellers
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

An approach and system for robust constraints handling for measured disturbances with uncertain preview on a prediction horizon in model predictive control with application in automotive control systems A constraints handling system may incorporate a mechanism for measuring disturbances of automotive control subsystems, a model predictive controller that provides model predictive control, connected to the a mechanism for measuring disturbances, and a sensor subsystem that collects information about an uncertainty of measured disturbances. The disturbances may have an uncertain preview over a prediction horizon. Estimates for the disturbances may be made of the preview.

Claims

1. A. robust constraints handling system comprising:

a mechanism for measuring disturbances of automotive control subsystems;

a model predictive controller that provides model predictive control, connected to the a mechanism for measuring disturbances; and

a sensor subsystem that collects information about an uncertainty of measured disturbances; and

wherein;

the disturbances have an uncertain preview over a prediction horizon; and

estimates for the disturbances are made of the preview over the prediction horizon.

2. The system of claim 1, wherein the model predictive controller controls multivariable systems under nominal conditions having no uncertainties.

3. The system of claim 1, wherein:

the best performance of constraints handling of the model predictive controller is with true predicted future trajectories of one or more disturbance variables for each subsystem of the engine; and

improved performance of constraints handling of the model predictive controller is from increased estimates of an expected uncertainty range of the disturbance variables over a prediction horizon of the model predictive controller.

4. The system of claim 3, wherein an uncertainty of a selected measured disturbance variable is approximated on the prediction horizon.

5. The system of claim 4, wherein original prescribed limits on the prediction horizon are reduced and in turn a probability of exceeding the original prescribed limits is decreased.

6. The system of claim 1, the model predictive controller is of one or more vehicle subsystems where handling of constraints is made in a presence of one or more measured disturbances having an uncertain prediction, the prediction being critical for operation of the one or more vehicle subsystems.

7. The system of claim 1, wherein the model predictive controller is hosted in an embedded control unit of an engine.

8. The system of claim 1, wherein one or more measured disturbance variables having an uncertain preview over the prediction horizon are selected from a group comprising vehicle speeds, pedal position of fuel control by a driver, driver'"'"'s torque requests, driver'"'"'s aggressive tip-ins, sudden vehicle accelerations, exhaust gas flow, and ambient conditions.

9. The system of claim 6, where one or more vehicle subsystems are selected from a group comprising thermal management, fuel injection, diesel oxidation catalyst control, diesel particulate filter control, aftertreatment, turbocharger, and air path of an engine.

10. A system for robust constraints handling for systems with measured. disturbances, comprising:

a vehicle having an engine;

one or more subsystems associated with the engine; and

a model predictive controller that provides model predictive control for the one or more subsystems; and

wherein;

the one or more subsystems have controlled variables to be maintained within limits by the model predictive controller;

the vehicle has disturbance variables that have uncertainties;

an expected uncertainty range of the disturbance variables over a prediction horizon of the model predictive control is estimated; and

an algorithm in the model predictive controller approximates uncertainties of selected variables on the prediction horizon, in which original prescribed limits on the prediction horizon are reduced relative to a worst case of a combination of values of disturbance variables to which a probability of exceeding the original prescribed limits is decreased.

11. The system of claim 10, wherein the algorithm provides robust constraints handling in a presence of measured disturbances of the selected variables having uncertain prediction as needed to improve operation of the one or more subsystems.

12. The system. of claim. 10, wherein the algorithm is a computer program in a controller as hosted on a control unit of the vehicle.

13. A method for handling constraints robustly for systems with measured disturbances, comprising:

controlling one or more subsystems of an engine with model predictive control;

obtaining information about uncertainties of disturbance variables that are measured;

previewing uncertainties of disturbance variables of the one or more subsystems over a prediction horizon in the model predictive control;

using model predictive control to control subsystems under conditions in absence of the uncertainties;

achieving best performance of the model predictive control when future trajectories of disturbance variables are predicted for the one or more subsystems;

estimating an uncertainty range of disturbance variables over a prediction horizon of the model predictive control;

handling constraints in view of disturbance variables that are measured and have uncertainties over the prediction horizon; and

improving handling of the constraints of the disturbance variables by approximating uncertainties of the disturbance variables on the prediction horizon, and thus reducing original limits of the disturbance variables on the prediction horizon.

14. The method of claim 13, wherein reducing the original prescribed limits on the prediction horizon for the disturbance values decreases a probability of violating the original limits of the disturbance variables.

15. The method of claim 13, wherein:

developing an algorithm in the model predictive controller that approximates uncertainties of selected variables on the prediction horizon, in which original prescribed limits on the prediction horizon are reduced relative to a worst case of a combination of values of disturbance variables to which a probability of exceeding the original prescribed limits is decreased; and

a probability of violating the original limits of disturbance variables is less for the model predictive control having the algorithm than a probability of violating the original limits of the disturbance variables for the model predictive control operating without the algorithm.