Abstract: Systems and methods for controlling automotive powertrains using a distributed control architecture are disclosed. A distributed control system may include a supervisory control unit for controlling one or more powertrain subsystems, and one or more subsystem control units in communication with the supervisory control unit. The supervisory control unit can be configured to execute a central optimization algorithm that computes variables from across multiple powertrain subsystems, and then outputs a number of globally approximated command values to each associated subsystem control unit. In some embodiments, the central optimization algorithm can be configured to solve a global cost function or optimization routine. One or more of the subsystem control units can be configured to execute a lower-level algorithm or routine, which can comprise a higher-fidelity model than that used by the central optimization algorithm.

Abstract: A method includes identifying a signal and a disturbance associated with a model. The signal and disturbance are identified using historical data associated with one or more process variables. The method also includes decomposing the signal and the disturbance at a plurality of resolution levels. The method further includes extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance. In addition, the method includes determining a quality of the model using the extracted data segments and at least a portion of the historical data.

Abstract: A method, system, and computer program are provided for filtering a signal. The method, system, and computer program receive a sample of a signal being filtered and identify a bias associated with the sample. The bias includes a cushion and an increment. The method, system, and computer program also output an expected value for the sample of the signal being filtered combined with a portion of the bias. The portion of the bias is based at least partially on a size of the cushion.

Abstract: A matrix includes samples associated with a first signal and samples associated with a second signal. The second signal includes a first portion associated with the first signal and a second portion associated with at least one disturbance, such as white noise or colored noise. A projection of the matrix is produced using canonical QR-decomposition. Canonical QR-decomposition of the matrix produces an orthogonal matrix and an upper triangular matrix, where each value in the diagonal of the upper triangular matrix is greater than or equal to zero. The projection at least substantially separates the first portion of the second signal from the second portion of the second signal.

Abstract: A method, apparatus, and computer program are provided for identifying possible indicators of a defective valve. The method, apparatus, and computer program decompose a signal having a plurality of process variable measurements into a plurality of resolution levels. The process variable measurements are associated with operation of a valve. The method, apparatus, and computer program group the resolution levels into a plurality of groups. The method, apparatus, and computer program identify one or more defect indicators for at least some of the resolution levels using the groups. The one or more defect indicators are associated with a possible defect in the valve.

Abstract: A method, apparatus, and computer program are provided for identifying a defective valve. The method, apparatus, and computer program identify one or more indicators of a possible defect in the valve at a plurality of resolution levels. The one or more indicators are identified using at least one of one or more operating characteristics associated with the valve. The method, apparatus, and computer program generate a plurality of indexes associated with the resolution levels, where the indexes are based on the one or more indicators and identify a likelihood of a valve defect. The method, apparatus, and computer program select one of the plurality of resolution levels using at least one of the indexes. The method, apparatus, and computer program determine an overall probability of a valve defect using at least one index associated with the selected resolution level.

Abstract: A method includes identifying a signal and a disturbance using historical data associated with one or more process variables. The method also includes decomposing the signal and the disturbance at a plurality of resolution levels. The method further includes identifying a plurality of points associated with the decomposed signal using the decomposed signal and the decomposed disturbance. In addition, the method includes selecting at least some of the points and extracting a plurality of data segments from the signal, where the data segments are associated with the selected points.

Abstract: A method, system, and computer program are provided for operating a valve using a cushion filter. The method, system, and computer program receive a process variable signal associated with operation of a valve. The method, system, and computer program filter the process variable signal to produce a filtered process variable signal using a cushion filter. The cushion filter implements a cushion in a bias used by the cushion filter. The method, system, and computer program generate an output signal for adjusting the valve based at least partially on the filtered process variable signal.