Abstract: An engine system incorporating an engine, one or more sensors, and a controller. The controller may be connected to the one or more sensors and the engine. The one or more sensors may be configured to sense one or more parameters related to operation of the engine. The controller may incorporate an air-path state estimator configured to estimate one or more air-path state parameters in the engine based on values of one or more parameters sensed by the sensors. The controller may have an on-line and an off-line portion, where the on-line portion may incorporate the air-path state estimator and the off-line portion may configure and/or calibrate a model for the air-path state estimator.

Abstract: An engine system incorporating an engine, one or more sensors, and a controller. The controller may be connected to the one or more sensors and the engine. The one or more sensors may be configured to sense one or more parameters related to operation of the engine. The controller may incorporate an air-path state estimator configured to estimate one or more air-path state parameters in the engine based on values of one or more parameters sensed by the sensors. The controller may have an on-line and an off-line portion, where the on-line portion may incorporate the air-path state estimator and the off-line portion may configure and/or calibrate a model for the air-path state estimator.

Abstract: An engine system incorporating an engine, one or more sensors, and a controller. The controller may be connected to the one or more sensors and the engine. The one or more sensors may be configured to sense one or more parameters related to operation of the engine. The controller may incorporate an air-path state estimator configured to estimate one or more air-path state parameters in the engine based on values of one or more parameters sensed by the sensors. The controller may have an on-line and an off-line portion, where the on-line portion may incorporate the air-path state estimator and the off-line portion may configure and/or calibrate a model for the air-path state estimator.

Abstract: An improved tool and technique for performance quality testing of a synthesized controller or a controller-in-development is disclosed. A controller's performance in a test run within a simulation testing environment is quantitatively compared to an optimal performance parameter as defined in a controller performance model. Deviation between these compared results is recorded as an indicator of poor controller performance. Only deviating test results are recorded for review to guide further fine tuning or modifications of controller settings, and to save mass storage space. The controller performance test runs autonomously and may be automatically restarted should any failure within the simulation environment occur.

Abstract: An engine system incorporating an engine, one or more sensors, and a controller. The controller may be connected to the one or more sensors and the engine. The one or more sensors may be configured to sense one or more parameters related to operation of the engine. The controller may incorporate an air-path state estimator configured to estimate one or more air-path state parameters in the engine based on values of one or more parameters sensed by the sensors. The controller may have an on-line and an off-line portion, where the on-line portion may incorporate the air-path state estimator and the off-line portion may configure and/or calibrate a model for the air-path state estimator.

Abstract: A method and system for updating tuning parameters associated with a controller without repetitive compilation of a controller code. The controller code represents an algorithm associated with the controller and can be compiled separately from a data set representing a solution for an optimization problem and also from a data set representing parameters required for prediction. The algorithm can be implemented in a programming language code suitable for implementation on an embedded platform or other types of computer platforms. The data sets can be represented in a specified data structure and the variables associated with the data structure can be declared in the controller template code. The variables can be updated independently without varying the compiled code associated with the controller algorithm that is referring to the variables. The controller can also be updated while the controller actively performs online.

Abstract: A method and system for updating tuning parameters associated with a controller without repetitive compilation of a controller code. The controller code represents an algorithm associated with the controller and can be compiled separately from a data set representing a solution for an optimization problem and also from a data set representing parameters required for prediction. The algorithm can be implemented in a programming language code suitable for implementation on an embedded platform or other types of computer platforms. The data sets can be represented in a specified data structure and the variables associated with the data structure can be declared in the controller template code. The variables can be updated independently without varying the compiled code associated with the controller algorithm that is referring to the variables. The controller can also be updated while the controller actively performs online.

Abstract: A method and system for updating tuning parameters associated with a controller without repetitive compilation of a controller code. The controller code represents an algorithm associated with the controller and can be compiled separately from a data set representing a solution for an optimization problem and also from a data set representing parameters required for prediction. The algorithm can be implemented in a programming language code suitable for implementation on an embedded platform or other types of computer platforms. The data sets can be represented in a specified data structure and the variables associated with the data structure can be declared in the controller template code. The variables can be updated independently without varying the compiled code associated with the controller algorithm that is referring to the variables. The controller can also be updated while the controller actively performs online.

Abstract: A method and system for combining a feedback control and a feedforward control in a linear MPC to minimize effect of model uncertainty. An externally computed feedforward signal, which is more accurate and reliable, can be utilized in association with the MPC, A steady state relation between system parameters can be determined in order to compute the feedforward signal for a set of actuators associated with a non-linear system. A feedback MPC controller can then be designed. A state observer can be configured as an unknown input observer to estimate the effect of the feedforward signal. A strategy for manipulating the constraints of the MPC feedback signal can be implemented. A resulting control action for the actuators can be provided as a sum of corresponding feedback and feedforward signal while ensuring the constraints satisfaction.

Abstract: An improved tool and technique for performance quality testing of a synthesized controller or a controller-in-development is disclosed. A controller's performance in a test run within a simulation testing environment is quantitatively compared to an optimal performance parameter as defined in a controller performance model. Deviation between these compared results is recorded as an indicator of poor controller performance. Only deviating test results are recorded for review to guide further fine tuning or modifications of controller settings, and to save mass storage space. The controller performance test runs autonomously and may be automatically restarted should any failure within the simulation environment occur.

Abstract: A method and system for updating tuning parameters associated with a controller without repetitive compilation of a controller code. The controller code represents an algorithm associated with the controller and can be compiled separately from a data set representing a solution for an optimization problem and also from a data set representing parameters required for prediction. The algorithm can be implemented in a programming language code suitable for implementation on an embedded platform or other types of computer platforms. The data sets can be represented in a specified data structure and the variables associated with the data structure can be declared in the controller template code. The variables can be updated independently without varying the compiled code associated with the controller algorithm that is referring to the variables. The controller can also be updated while the controller actively performs online.

Abstract: A method and system for combining a feedback control and a feedforward control in a linear MPC to minimize effect of model uncertainty. An externally computed feedforward signal, which is more accurate and reliable, can be utilized in association with the MPC. A steady state relation between system parameters can be determined in order to compute the feedforward signal for a set of actuators associated with a non-linear system. A feedback MPC controller can then be designed. A state observer can be configured as an unknown input observer to estimate the effect of the feedforward signal. A strategy for manipulating the constraints of the MPC feedback signal can be implemented. A resulting control action for the actuators can be provided as a sum of corresponding feedback and feedforward signal while ensuring the constraints satisfaction.