To control a hybrid dynamical system, a predictive feedback controller formulates a mixed-integer nonlinear programming (MINLP) problem including nonlinear functions of continuous optimization variables representing the continuous elements of the operation of the hybrid dynamical system and/or one or multiple linear functions of integer optimization variables representing the discrete elements of the operation of the hybrid dynamical system. The MINLP problem is formulated into a separable format ensuring that the discrete elements of the operation are present only in the linear functions of the MINLP problem. The MINLP problem is solved over multiple iterations using a partial convexification of a portion of a space of the solution including a current solution guess. The partial convexification produces a convex approximation of the nonlinear functions of the MINLP without approximating the linear functions of the MINLP to produce a partially convexified MINLP.

A method for recording data relating to the performance of an oil and gas flow network uses statistical data to represent raw data in a compact form. Categories are assigned to time intervals in the data. The method comprises: (1) gathering data covering a period of time, wherein the data relates to the status of one or more control point(s) within the flow network and to one or more flow parameter(s) of interest in one or more flow path(s) of the flow network; (2) identifying multiple time intervals in the data during which the control points and the flow parameter(s) can be designated as being in a category selected from multiple categories; (3) assigning a selected category of the multiple categories to each one of the multiple datasets that are framed by the multiple time intervals; and (4) extracting statistical data representative of some or all of the datasets identified in step (2) to thereby represent the original data from step (1) in a compact form including details of the category assigned to each time interval in step (3).

A method of tuning a prediction model relating to at least one particular configuration of a manufacturing device. The method includes obtaining a function including at least a first function of first prediction model parameters associated with the at least one particular configuration, and a second function of the first prediction model parameters and second prediction model parameters associated with configurations of the manufacturing device and/or related devices other than the at least one particular configuration. Values of the first prediction model parameters are obtained based on an optimization of the function, and a prediction model is tuned according to these values of the first prediction model parameters to obtain a tuned prediction mode.

A control mechanism scheduler for a water resource infrastructure receives operating data and disturbance data, the operating data describing infrastructure components of the water resource infrastructure, the disturbance data comprising a disturbance signal describing a disturbance expected to disturb the water resource infrastructure. The control mechanism scheduler generates classes for disturbance signals, generates simulations of the water resource infrastructure, and generates schedules of setpoints for control mechanisms actuable to control the infrastructure components of the water resource infrastructure in accordance with approaching a predetermined objective.

A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.

A method is disclosed for steering a physical analog system (e.g., an electric motor) using a discrete-level (e.g., binary) control signal. The discrete-level control signal is computed by an iterative scheme that can handle a long planning horizon. A preference for infrequent level switches can be taken into account. The quality of the fit to the target trajectory can be expressed not only by the quadratic error, but also by other norms. The method can be used also for digital-to-analog conversion.

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

A method for dispatching an operation of a distribution feeder of electrical power into a grid with heterogeneous prosumers, the method comprising the steps of establishing a dispatch plan on a computer by using forecast data of an aggregated consumption and local distributed generation at the grid for a predetermined period, and operating the distribution feeder according to the established dispatch plan during the predetermined period.

A system and method include a controller configured to obtain or receive a predicted weather event along a route during a current or upcoming trip based on weather data. The controller determines a weather fitness of a first vehicle for traveling on the route during the trip based on the predicted weather event and equipment characteristics of equipment disposed onboard the first vehicle. The controller assigns one of the first vehicle or a different, second vehicle to complete the trip based at least in part on the weather fitness of the first vehicle.

Estimation algorithms, methods, and systems are provided that estimate the internal temperatures inside of a part being built using powder bed fusion (PBF). Closed-loop state estimation is applied to the problem of monitoring temperature fields within parts during the PBF build process. A simplified linear time-invariant (LTI) model of PBF thermal physics with the properties of stability, controllability and observability is presented. In some aspects, Model Predictive Control (MPC) may be used as an expanded application of an Ensemble Kalman Filter (EnKF) methods to control the PBF process. MPC is used to forecast the PBF build process behavior N time steps into the future and identifies inputs that drive the temperature of a corresponding node in a mesh of n nodes towards a predetermined target temperature. The inputs are