Abstract: 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.

Abstract: 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 predicted parameters based on a model of a subsystem and a set of possible target values. The MPC module generates a cost for the set of possible target values based on the predicted parameters and at least one of weighting values and references values. The MPC module adjusts the at least one of the weighting values and the reference values based on a desired rate of change in an operating condition of the subsystem. The MPC module selects the set of possible target values from multiple sets of possible target values based on the cost. The actuator module adjusts an actuator of the subsystem based on at least one of the target values.

Abstract: Method for creating digital circuits of an MPC controller (2), which implements an approximation technique for Model Predictive Control (MPC), in which a quadratic programming (QP) or linear programming (LP) optimization problem is formulated by starting from a model defined over a set of states of a state (x), wherein the said set of states is partitioned into simplices identified by vertices (v), and wherein said method comprises the steps of a) compute a solution (w*) of the optimization problem and define a control law (u(x)); b) check the stability under the control law (u(x)); c) synthesize a digital circuit by starting from the control law (u(x)) and the vertices (vi).

Abstract: A system according to the present disclosure includes a model predictive control (MPC) module, an actuator module, and a remedial action module. The MPC module performs MPC tasks that include predicting operating parameters for a set of possible target values and determining a cost for the set of possible target values based on the predicted operating parameters. The MPC tasks also include selecting the set of possible target values from multiple sets of possible target values based on the cost and setting 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. The remedial action module selectively takes a remedial action based on at least one of an amount of time that elapses as the MPC tasks are performed and a number of iterations of the MPC tasks that are performed.

Abstract: A system according to the principles of the present disclosure includes a desired capacity module, an anticipated torque request module, and an engine actuator module. The desired capacity module generates a desired torque capacity of an engine at a future time based on a present torque request and a maximum torque output of the engine. The anticipated torque request module generates an anticipated torque request based on the desired torque capacity. The engine actuator module controls an actuator of the engine at a present time based on the anticipated torque request.

Abstract: A control system includes a control module that receives a first request corresponding to a control value for at least one of a plurality of actuators, selectively receives a second request associated with a predicted future control value for at least one of the plurality of actuators, determines a target value for the actuator based on the first request if the second request was not received, and generates a reference signal representing the second request if the second request was received. The reference signal indicates at least one of a predicted increase in the control value and a predicted decrease in the control value. A model predictive control module receives the reference signal and adjusts one of the plurality of actuators associated with the predicted future control value based on the reference signal.

Abstract: 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.

Abstract: 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.

Abstract: 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 predicted parameters based on a model of a subsystem and a set of possible target values. The MPC module generates a cost for the set of possible target values based on the predicted parameters and at least one of weighting values and references values. The MPC module adjusts the at least one of the weighting values and the reference values based on a desired rate of change in an operating condition of the subsystem. The MPC module selects the set of possible target values from multiple sets of possible target values based on the cost. The actuator module adjusts an actuator of the subsystem based on at least one of the target values.

Abstract: 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.

Abstract: Method for creating digital circuits of an MPC controller (2), which implements an approximation technique for Model Predictive Control (MPC), in which a quadratic programming (QP) or linear programming (LP) optimization problem is formulated by starting from a model defined over a set of states of a state (x), wherein the said set of states is partitioned into simplices identified by vertices (v), and wherein said method comprises the steps of a) compute a solution (w*) of the optimization problem and define a control law (u(x)); b) check the stability under the control law (u(x)); c) synthesize a digital circuit by starting from the control law (u(x)) and the vertices (vi).