Abstract: A system that provides an estimation of quantities, which are not necessarily directly measurable in a subsystem. The estimates of these quantities may be inferred from other variables. This approach may be referred to as inferential sensing. Physical sensors may be replaced with models or virtual sensors, also known as inferential sensors. The present approach may be a framework for designing inferential sensors in automotive subsystems. The framework may incorporate preparing a model for an observed subsystem, populating a real-time template with data, and running an inferential sensor periodically together with a model in real-time to obtain estimated variables. Once implemented, the framework may be reused for virtually any automotive subsystem without needing significant software code changes.

Abstract: A system and method for solving a quadratic programming optimization problem with bound constraints using a semi-explicit QP solver with respect to an embedded platform is presented. A linear system of equations associated with a matrix (e.g., a Karush-Kuhn-Tucker matrix, KKT system) can be solved at each iteration of the solver based on a factorization approach. A set of partial factors with respect to the QP problem can be pre-computed off-line and stored into a memory. The factorization process of the KKT matrix can then be finished on-line in each iteration of the semi-explicit QP solver in order to effectively solve the QP optimization problems. The QP problem can be solved utilizing a standard active-set approach and/or a partial explicit approach based on a processor utilization and memory usage.

Abstract: A system and method for solving a quadratic programming optimization problem with bound constraints using a semi-explicit QP solver with respect to an embedded platform is presented. A linear system of equations associated with a matrix (e.g., a Karush-Kuhn-Tucker matrix, KKT system) can be solved at each iteration of the solver based on a factorization approach. A set of partial factors with respect to the QP problem can be pre-computed off-line and stored into a memory. The factorization process of the KKT matrix can then be finished on-line in each iteration of the semi-explicit QP solver in order to effectively solve the QP optimization problems. The QP problem can be solved utilizing a standard active-set approach and/or a partial explicit approach based on a processor utilization and memory usage.