A system and approach for storing factors in a quadratic programming solver of an embedded model predictive control platform. The solver may be connected to an optimization model which may be connected to a factorization module. The factorization module may incorporate a memory containing saved factors that may be connected to a factor search mechanism to find a nearest stored factor in the memory. A factor update unit may be connected to the factor search mechanism to obtain the nearest stored factor to perform a factor update. The factorization module may provide variable ordering to reduce a number of factors that need to be stored to permit the factors to be updated at zero floating point operations per unit of time.

A computer-implemented method for determining a control trajectory for a robotic device. The method includes: performing an information theoretic model predictive control applying a control trajectory sample prior in each time step to obtain a control trajectory for a given time horizon; determining the control trajectory sample prior depending on a data-driven trajectory prediction model which is trained to output a control trajectory sample as the control trajectory sample prior based on an actual state of the robotic device.

Systems, methods, apparatuses, and computer program products for a greenhouse indoor environment controller based on model predictive control (MPC), which can be integrated into existing greenhouse regulatory systems to optimally maintain critical climatic variables, including artificial lighting levels, CO.sub.2, indoor temperature, and humidity levels within acceptable limits. The objectives of the MPC may be to maximize the rate of crop photosynthesis while optimizing the use of the available water and energy resources, taking into account the unpredictability and intermittent nature of renewable energies and external atmospheric conditions. Accordingly, certain embodiments may facilitate the management of greenhouses by anticipating control actions for a better quality of production. For that, mathematical formulations of the optimal control problem may be described, and the numerical results related to the application of the MPC to case studies are described integrating the effects of greenhouse structural considerations and the influence of climate data on its operation.

A machine monitoring device is provided which includes (a) a communications link to interrogate a machine with a probe signal and receive one or more measured machine operating condition outputs; and (b) a device controller capable of (i) selecting a machine operating condition input variable for which a corresponding machine operating condition output is unknown; (ii) applying a predictive model in which the machine operating condition input variable serves as an argument of a predicted machine operating condition output; (iii) updating a library of predicted machine operating condition outputs; and (iv) alerting a human operator if a measured or predicted machine operating condition output exceeds a predetermined limit. The predictive model is based on at least two independent primary models having at least one correspondence between a primary model machine operating condition and a corresponding machine output. The primary models share a common basis in the predictive model.

A system including a power plant having thermal generating units that operate according to multiple possible operating modes, which are differentiated by a unique operational or maintenance schedule. The system further includes a hardware processor and machine readable storage medium on which is stored instructions that cause the hardware processor to execute a process related to optimizing the operational or maintenance schedule during a selected operating period. The process may include: receiving the selected operating period; selecting competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode.

A model is generating for predicting energy workloads to adjust electrical energy supply to meet varying short-term energy demands at a microcosm level. A model is developed, using a computer, to facilitate predicting energy workloads for adjusting energy supplies to meet an energy demand. The model includes receiving, at the computer, input parameters of dynamic values of workloads as historical data, and generating a predictive model by analyzing the input parameters. The model further includes predicting short-term energy demands based on the predictive model, the predicted short-term energy demands include identifying a predicted peak value. Also, the model includes initiating short term energy output in an electrical grid to a transformer level component in the electrical grid based on the predicted short term energy demands.

Processes and systems for controlling operation of a commercial refinery distillation column and/or splitter operable to separate hydrocarbons. An automated process controller (APC) receives signal from at least one analyzer that provides information about the concentration of at least a first chemical in a first fraction and a second chemical in a second fraction obtained from the distillation column. The APC comprises programming in the form of an algorithm that calculates real-time monetary values for the first chemical and the second chemical and alters the operation of the distillation column to change either the percentage of the first chemical in the second fraction or the percentage of the second chemical in the first fraction, thereby maximizing overall operational profit for the distillation column.

System, methods, and computer-readable media for training an object path prediction model to reduce an uncertainty of a predicted path when the predicted path of an object adjacent to another object. The training penalizes an uncertainty area prediction associated with a predicted future location of a nearby object to an autonomous vehicle (AV) when the uncertainty area prediction overlaps with another object to which the first detected object would be adjacent at the predicted future location. The training also penalizes a set of predicted future locations that implies improbable vehicle kinematics, whereby the object path prediction model becomes trained to avoid predicting similar sets of predicted future locations with improbable vehicle kinematics.

A method includes receiving data indicative of properties of a substrate support from one or more sensors of a removable sensor assembly disposed proximate to the substrate support. The method further includes providing data based on the data indicative of properties of a substrate support to a physics-based model of the substrate support. The method further includes receiving predicted performance data of the substrate support from the physics-based model.

A method includes receiving first data indicative of a range of values of a quality parameter of a type of manufacturing chamber component. Each value in the range of values meets one or more threshold criteria. The method further includes providing the first data to a physics-based model of a manufacturing chamber. The method further includes receiving, from the physics-based model, second data indicating a relationship between values of the quality parameter and predicted conditions in the manufacturing chamber. The method further includes determining, based on the relationship between values of the quality parameter and the predicted conditions, whether a new manufacturing chamber component of the manufacturing chamber component type is to be installed in the manufacturing chamber.