# US 10,503,128 B2 · Approach and system for handling constraints for measured disturbances with uncertain preview > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,503,128 B2 - Document ID: US10503128B2 - Status: Granted - Kind: B2 - Application number: 15/005,406 - Filed: 2016-01-25 - Published: 2016-07-28 - Granted: 2019-12-10 - Earliest claimed: 2015-01-28 - Assignees: Garrett Transportation I Inc. - Applicants: Honeywell spol. s.r.o. - Inventors: Jaroslav Pekar; Jaroslav Beran - Web page: https://patentgrep.com/patent/US10503128B2 ## CPC - G05B 13/048: {using a predictor} - F02D 29/02: peculiar to engines driving vehicles; peculiar to engines driving variable pitch propellers - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - F02D: CONTROLLING COMBUSTION ENGINES (vehicle fittings, acting on a single sub-unit only, for automatically controlling vehicle speed B60K31/00; conjoint control of vehicle sub-units of different type or different function, road vehicle drive control systems for purposes other than the control of a single sub-unit B60W) ## 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 ### Claim 1 An engine subsystem control system comprising: a mechanism for measuring disturbances variables of automotive control subsystems; a model predictive controller that provides model predictive control of the automotive control subsystems to achieve prescribed performance while maintaining controlled variables within prescribed limits based on measurements of the disturbance variables and the controlled variables, the model predictive controller is connected to the mechanism for measuring disturbance variables and is configured to receive values from the mechanism based on the measurements of the disturbance variables; and wherein: the disturbance variables are uncertain over a prediction horizon; the model predictive controller provides estimates of the disturbance variables over the prediction horizon; the model predictive controller adjusts a range between limits on the disturbance variables based on the measurements of the disturbance variables-during operation of the automotive control subsystems; and the model predictive controller adjusts automotive control subsystem operations to achieve the prescribed performance based on the adjusted range between limits on the disturbance variables. ### Claim 2 (depends on 1) The system of claim 1, wherein the model predictive controller controls multivariable systems under nominal conditions. ### Claim 3 (depends on 1) The system of claim 1, wherein an uncertainty of a selected measured disturbance variable is approximated on the prediction horizon. ### Claim 4 (depends on 1) 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 disturbance variables having an uncertain prediction. ### Claim 5 (depends on 1) The system of claim 1, wherein the model predictive controller is hosted in an embedded control unit of an engine. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 4) The system of claim 4, 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. ### Claim 8 A system for controlling subsystems of an engine, 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 to achieve prescribed performance while maintaining controlled variables within prescribed limits based on disturbance variables of the vehicle and the controlled variables; and wherein: the one or more subsystems have controlled variables to be maintained within a range of limits by the model predictive controller; the disturbance variables have uncertainties; the model predictive controller approximates uncertainties of selected disturbance variables on the prediction horizon and reduces an original prescribed range between limits on the selected disturbance variables over the prediction horizon based on measurements of the disturbance variables during operation of the vehicles; and the model predictive controller controls operation of the one or more subsystems associated with the engine based on the reduced range between limits for the selected variables. ### Claim 9 (depends on 8) The system of claim 8, wherein the algorithm provides robust constraints handling in a presence of measured disturbances of the selected variables having uncertainties. ### Claim 10 (depends on 8) The system of claim 8, wherein the algorithm is a computer program in a controller as hosted on a control unit of the vehicle. ### Claim 11 A method for controlling subsystems of an engine, comprising: controlling one or more subsystems of an engine with model predictive controller to achieve prescribed performance while maintaining controlled variables within prescribed limits based on disturbance variables and the controlled variables; previewing uncertainties of disturbance variables of the one or more control subsystems over a prediction horizon in the model predictive controller; using the model predictive controller to adjust the one or more control subsystems based the disturbance variables of the control subsystems under conditions having initial constraints in an absence of the uncertainties of the disturbance variables over the prediction horizon; estimating an uncertainty range of the disturbance variables over the prediction horizon with the model predictive controller when the disturbance variables have uncertainties over the prediction horizon; offsetting the constraints of the disturbance variables by approximating uncertainties of the disturbance variables on the prediction horizon based on measurements of the disturbance variables during operation of the engine, and thus reducing an original range between limits on the disturbance variables on the prediction horizon; and adjusting, with the model predictive controller, the one or more control subsystems based on the offset constraints of the disturbance variables. ### Claim 12 (depends on 11) The method of claim 11, wherein reducing the original prescribed range of limits on the prediction horizon for the disturbance values decreases a probability of violating the original range of limits of the disturbance variables. ### Claim 13 (depends on 11) The method of claim 11, wherein: developing an algorithm in the model predictive controller that approximates uncertainties of selected variables on the prediction horizon, in which original prescribed range of limits on the prediction horizon are reduced; and a probability of violating the original range of 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.