Gm Global Technolgoy Operations, Inc.

Ruixing Long

Ning Jin

Julian R Verdejo

Yue-Yun Wang

Jyh-shin Chen

Alberto Bemporad

Daniele Bernardini

Hualin Tan

SeyedAlireza Kasaiezadeh Mahabadi

Yiran Hu

Nicola Pisu

Lei Hao

Christopher E Whitney

Paul G Otanez

Bo Yu

Kevin C Wong

Naser Mehrabi

Gary Robert Cygan, Jr

David N Hayden

Hassan Askari

Saurabh Kapoor

Bojian Cao

Bharath Pattipati

Shamim Mashrouteh

Daniel Lee Baibak

Jun Chen

Mansour Ataei

Amir Abolhassani

A data health monitoring and recording system for a vehicle includes a data recording infrastructure and one or more controllers. The one or more controllers include a multi-layer control software architecture including two or more software layers. The one or more controllers execute instructions to monitor, by a health monitoring structure that is part of the multi-layer control software architecture, one or more parameters calculated by each of the two or more software layers. The one or more controllers compare a respective parameter of a respective software layer with a predetermined corresponding performance metric for a required time horizon. In response to determining the respective parameter of the respective software layer does not meet the predetermined corresponding performance metric for the required time horizon, a trigger that instructs the data recording infrastructure to record the parameters calculated by each of the two or more software layers is generated.

Vehicle data health monitoring and recording system for multi-layer control software architecture

A driver command interpreter system for a vehicle includes one or more controllers that execute instructions to receive a plurality of dynamic variables, vehicle configuration information, and driving environment conditions, and determine a target vehicle state during transient driving conditions based on the plurality of dynamic variables from the one or more sensors, the vehicle configuration information, and the driving environment conditions. The one or more controllers build a transient vehicle dynamic model based on the target vehicle state during transient driving conditions, the plurality of dynamic variables, the vehicle configuration information, and the driving environment conditions, and solve for desired zeros corresponding to the target vehicle state during transient conditions.

Driver command interpreter system determining achievable target vehicle state during steady-state and transient driving conditions

The concepts described herein relate to a calculation of desired future longitudinal horizons related to torque or acceleration, and desired future lateral horizons related to yaw rate and lateral velocity, and their use in response to driver-selectable modes. In the longitudinal direction, driver inputs of pedal and brake position as well as drivability metrics are used to calculate the desired future torque trajectory. In the lateral direction, the front and rear steering angles may be used with a bicycle model to derive the trajectories. The trajectories are used in a vehicle motion controller that uses weighting to tradeoff competing requests and deliver performance that is consistent with a selected driver mode, such as a tour mode, a sport mode, an off-road mode, a trailering mode, etc.

Method and system for controlling vehicle operation

A method and apparatus for electric motor control includes a model predictive controller operating in a d-q reference frame to provide d-q reference frame voltage command signals that counteract a magnetic cross coupling within the motor.

Method and apparatus for electric motor control

A method for controlling an actuator system of a motor vehicle includes utilizing a model predictive control (MPC) module with an MPC solver to determine optimal positions of one or more actuators of the actuator system. The method further includes receiving a plurality of actuator system parameters, and triggering the MPC solver to generate one or more control commands from plurality of actuator system parameters. The method further includes applying a cost function to reduce a steady-state tracking error in the one or more control commands from the MPC solver and applying the one or more control commands to alter positions of the one or more actuators, and applying a penalty term to the steady-state predictions of positions of the plurality of actuators to limit a difference between a steady-state prediction of the actuator system and a solution from the MPC solver.

Method for increasing control performance of model predictive control cost functions

A method, control system, and propulsion system use model predictive control to control and track several parameters for improved performance of the propulsion system. Numerous sets of possible command values for a set of controlled variables are determined. Initial constraints for the controlled variables are determined, which include upper and lower limits for each controlled variable and upper and lower rate-of-change limits for each controlled variable. A set of consolidated constraint limits for the controlled variables is then determined. Each consolidated constraint limit is determined by consolidating one of the upper and lower limits with one of the upper and lower rate-of-change limits. A cost for each set of possible command values is determined, and the set of possible command values that has the lowest cost and falls within the set of consolidated constraint limits is selected for use in controlling the propulsion system.

Consolidation of constraints in model predictive control

An LPV/MPC engine control system is disclosed that includes an engine control unit connected to multiple sensors. The engine control unit receives, from the sensors, signals indicative of desired engine torque and engine torque output, and determines, from these signals, optimal engine control commands using a piecewise LPV/MPC routine. This routine includes: determining a nonlinear and a linear system model for the engine assembly, minimizing a control cost function in a receding horizon for the linear system model, determining system responses for the nonlinear and linear system models, determining if a norm of an error function between the system responses is smaller than a calibrated threshold, and if the norm is smaller than the predetermined threshold, applying the linearized system model in a next sampling time for a next receding horizon to determine the optimal control command. Once determined, the optimal control command is output to the engine assembly.

Linear parameter varying model predictive control for engine assemblies

A method for controlling an actuator system of a motor vehicle includes utilizing a model predictive control (MPC) module with an MPC solver to determine optimal positions of a plurality of actuators subject to constraints, optimizing a cost function for a set of actuator duty cycles for controlling positions of the plurality of actuators, determining if the MPC solver has determined optimal actuator positions for the plurality of actuators, and applying a linear quadratic regulator (LQR) solution if the MPC solver fails to determine optimal actuator positions for the plurality of actuators.

Model predictive control systems and methods for increasing computational efficiency

A system according to the principles of the present disclosure includes a model predictive control (MPC) module and an actuator module. The MPC module generates a set of possible target values for an actuator of an engine and predicts an operating parameter of the engine for each of the possible target values. The MPC module determines a weighting value associated with each of the target values based on a corresponding iteration number and determines a cost for the set of possible target values based on the predicted operating parameters and the weighting values. The MPC module selects the set of possible target values from multiple sets of possible target values based on the cost and sets target values to the possible target values of the selected set. The actuator module controls an actuator of an engine based on at least one of the target values.

System and method for adjusting weighting values assigned to errors in target actuator values of an engine when controlling the engine using model predictive control

A requesting module generates a first torque request for an engine based on driver input. A conversion module converts the first torque request into a second torque request. A model predictive control (MPC) module determines a current set of target values based on the second torque request, a model of the engine, a tableau matrix, and a basic solution matrix. The MPC module: initializes the basic solution matrix to a predetermined matrix that is dual feasible; selectively iteratively updates the basic solution matrix and columns of the tableau matrix; determines changes for the target values, respectively, based on entries of the basic solution matrix resulting from the selective iterative updating; and determines the current set of target values by summing the changes with a last set of target values, respectively. An actuator module controls an engine actuator based on a first one of the current set of target values.

A model predictive control (MPC) module performs multiple iterations to determine a current set of target values for an engine based on a torque request, a model of the engine, a tableau matrix, and a basic solution matrix. Each of the iterations includes: determining whether the basic solution matrix is not in standard form; when the basic solution matrix is not in standard form, identifying a first column of the tableau matrix based on a first entry position of the primal variable of the non-basic pair; when the basic solution matrix is in standard form and one or more primal variables of the basic solution matrix are negative, identifying the first column of the tableau matrix based on a second entry position of a dual variable of the basic pair; and selectively updating the basic solution matrix based on the first column of the tableau matrix.

Model Predictive control systems and methods for increasing computational efficiency

A torque requesting module generates a first torque request for a spark ignition engine based on driver input. A torque conversion module converts the first torque request into a second torque request. A model predictive control (MPC) module determines a set of target values based on the second torque request, a model of the engine, and a matrix having dimensions of (m+n) by (m+n). n is an integer greater than zero that is equal to a number of lower boundary constraints used in the determination of the set of target values. m is an integer greater than zero that is equal to a number of constraints used in the determination of the set of target values other than the lower boundary constraints. An actuator module controls opening of an engine actuator based on a first one of the target values.

Model predictive control systems and methods for internal combustion engines

A prediction module generates predicted engine operating parameters for a set of possible target values based on a plurality of values indicative of states of the engine and a first set of predetermined values set based on characteristics of the engine. A parameter estimation module determines one or more estimated operating parameters of the vehicle based on the plurality of values indicative of states of the engine and a second set of predetermined values. A cost module determines a cost for the set of possible target values based on the predicted engine operating parameters. A selection module, based on the cost, selects the set of possible target values from a group including the set of possible target values and N other sets of possible target values, wherein N is an integer greater than zero, and sets target values based on the selected set of possible target values.

Estimation systems and methods with model predictive control

An engine control method includes: generating a first predicted engine output torque and a first predicted mass of air per cylinder (APC) based on a model of the spark ignition engine and a first set of possible target values determined based on an engine torque request; generating a second predicted engine output torque and a second predicted mass of APC based on the model of the spark ignition engine and a second set of possible target values determined based on the engine torque request; determining a first cost for the first set of possible target values; determining a second cost for the second set of possible target values; selecting one of the first and second sets based on the first and second costs; and setting target values based on the possible target values of the selected one of the first and second sets.

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Windsor, CA, United States of America

Ruixing Long