Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to generate vehicle-level commands based on received vehicle operation commands. The received vehicle operation commands can comprise input commands corresponding to at least one of an autonomous vehicle (AV) mode of operation or a manual mode of operation. The processor is also programmed to generate target actuator commands based on the vehicle-level commands and transmit the target actuator commands to at least one actuator.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system for estimating a lateral velocity and a longitudinal velocity of a vehicle includes a plurality of sensors for monitoring data indicative of a travel state of the vehicle and one or more controllers in electronic communication with the plurality of sensors. The one or more controllers executes instructions to receive the data indicative of the travel state of the vehicle from the plurality of sensors. The one or more controllers estimate at least one initial estimated state of the vehicle based on the data indicative of the travel state of the vehicle. The one or more controllers fuse together the data indicative of the travel state of the vehicle with the at least one initial estimated state of the vehicle to determine the lateral velocity and a longitudinal velocity of the vehicle based on a single state estimation scheme.

Abstract: A system for managing chassis and driveline actuators of a motor vehicle includes a control module executing program code portions that: cause sensors 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, and then estimate actuator actions based on the vehicle state information, generate one or more control action constraints based on the vehicle state information and estimated actuator actions, generate a reference control action based on the vehicle state information, the estimated actions of the one or more actuators and the control action constraints, and integrate the vehicle state information, the estimated actuator actions, desired dynamic output, reference control action and the control action constraints to generate an optimal control action that falls within a range of predefined actuator capacities and ensures driver control of the vehicle.

Abstract: A system for adaptive tire force prediction in a motor vehicle includes a control module that executes program code portions that receive real-time static and dynamic data from motor vehicle sensors, that model forces at each tire of the motor vehicle at one or more incremental time steps, that estimate actual forces at each tire of the motor vehicle at each of the one or more incremental time steps, that adaptively predict tire forces at each tire of the motor vehicle at each of the one or more incremental time steps, that generate one or more control commands for actuators of the motor vehicle, that capture discrepancies between real-time force estimations and nominal force calculations at each tire of the motor vehicle, and that apply compensation parameters to reduce tracking errors in the one or more control commands to the one or more actuators of the motor vehicle.

Abstract: Presented are control systems for operating dual-independent drive unit (DIDU) powertrains, methods for making/operating such systems, and electric-drive vehicles with fault management and mitigation for DIDU axles. A method of operating a motor vehicle with a DIDU axle includes monitoring first and second drive units (DU) that are independently operable to drive respective road wheels via respective axle shafts of the DIDU axle. A vehicle controller receives an indication of a fault condition in the first DU from a fault sensing module and responsively determines a fault type for the fault condition. The controller ascertains the vehicle's current speed and determines a respective torque limit for each of the DIDU drive units based on the fault type and current vehicle speed. Torque output of the first DU is concomitantly constrained to a first torque limit while torque output of the second DU is constrained to a second torque limit.

Abstract: A motor vehicle includes first and second drive axles coupled to respective sets of road wheels, torque actuators inclusive of rotary electric machines configured to transmit respective output torques to the drive axles, and a main controller in communication with the torque actuators. The controller receives vehicle inputs indicative of a total longitudinal and lateral motion request. In response, the controller calculates a total longitudinal torque request and/or a total longitudinal speed request, a yaw rate request, and a lateral velocity request, then determines, using a cost optimization function, a torque vector for allocating the total longitudinal torque request and/or speed request, the yaw rate request, and the lateral velocity request to the drive axles within predetermined constraints. The controller also transmits a closed-loop control signal to each torque actuator or local controllers thereof to apply the torque vector via the drive axles.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An engine assembly includes an engine, a compressor, a turbine and a waste gate valve. A controller has a processor and a tangible, non-transitory memory on which is recorded instructions for executing a method of air path control based on an air path model. The controller is configured to determine a turbine power (Pt) as a function of a first factor (x1) and a second factor (x2). A compressor power (Pc) is determined as a function of a third factor (y1) and a fourth factor (y2). The controller is configured to control at least one of an intake throttle pressure (pth) and an intake manifold pressure (pi) by varying at least one of the first through fourth factors (x1, x2, y1, y2). The engine output is controlled based on at least one of the intake throttle and manifold pressures.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.