Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.

Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.

A method for controlling continuous and discrete actuators (e.g., modes) in a powertrain system includes receiving preview information from a sensor(s) describing an upcoming dynamic state at a future time point, and providing control inputs for the actuators to a controller that includes the preview information. The input set collectively describes a future torque or speed output state at the future time point. The controller processes the input set via a dynamical predictive model, in real time, to determine control solutions to take at the present time point for implementing the dynamic state at the future time point. A lowest opportunity cost control solution is determined and optimized. The controller executes the optimized solution at the present time step.

A controller for controlling a system with continuous and discrete elements of operation accepts measurements of a current state of the system, solves a mixed-integer model predictive control (MI-MPC) problem subject to state constraints on the state of the system to produce control inputs to the system, and submits the control inputs to the system thereby changing the state of the system. To solve the MI-MPC, the controller transforms the state constraints into state-invariant control constraints on the control inputs to the system, such that any combination of values for the control inputs, resulting in a sequence of values for the state variables that satisfy the state constraints, also satisfy the state-invariant control constraints, and solve the MI-MPC problem subject to the state constraints and the state-invariant control constraints.

A power system energy management system with dynamic state estimation (DSE) is disclosed wherein system dynamic states are estimated using SCADA measurements, PMU measurements, signals of controllers, digital recorders, protection devices, and smart electronic devices. The DSE is solved first by Unscented Kalman Filter, and if the Unscented Kalman Filter is failed, weighted lease square is used to solve the DSE. If weighted lease square is failed, integration method is used to calculate the dynamic states. In another aspect, Unscented Kalman Filter, weighted lease square, and integration calculation are applied to solve the DSE by nodal parallel computing for each generation system.

A building management system including building equipment operable to affect a variable state or condition of a building. The building management system includes a controller including a processing circuit. The processing circuit is configured to obtain an energy prediction model (EPM) for predicting energy requirements over time. The processing circuit is configured to monitor one or more triggering events to determine if the EPM should be retrained. The processing circuit is configured to, in response to detecting that a triggering event has occurred, identify updated values of one or more hyper-parameters of the EPM. The processing circuit is configured to operate the building equipment based on the EPM.

A controller for operating building equipment of a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including obtaining a first setpoint trajectory from a cloud computation system. The first setpoint trajectory includes setpoints for the building equipment or for a space of the building. The setpoints correspond to time steps of an optimization period. The operations include determining whether a connection between the controller and the cloud computation system is active or inactive at a time step of the optimization period and determining an active setpoint for the time step of the optimization period using either the first or second setpoint trajectory based on whether the connection between the controller and the cloud computation system is active or inactive at the time step. The operations include operating the building equipment based on the active setpoint.

A controller for a plant that exhibits nonlinear dynamics includes one or more processors and memory storing instructions that cause the one or more processors to perform operations. The operations include training a neural network model during an offline learning period using historical plant data representing a plurality of different historical states of the plant and using the neural network model during online operation of the plant to generate a linear predictor as a function of a current state of the plant, the linear predictor defining a linearization of the nonlinear dynamics localized at the current state of the plant. The controller controls equipment that operate to affect the current state of the plant by performing a predictive control process that uses the linear predictor to generate values of one or more manipulated variables provided as inputs to the equipment.

Properties of a physical system are measured and used to update estimated states of the system in an iterative manner. At each iteration, a state update weight is assigned for each state and the states are predicted from previous estimated states. System states are predicted from prior estimates and then updated dependent upon the measurements and the state update weights to provide updated estimated states. In addition, a prior covariance matrix of state errors is updated dependent upon the state update weights to provide an estimation error covariance matrix that is consistent with the updated estimated states. The updated state may be equivalent to a weighted sum of a prior estimated state and an initial updated estimated state. The approach provides improvements to a variety of estimators, including least squares estimators and estimators such as the Extended, Schmidt and Unscented Kalman Filters and the Rao-Blackwellized Particle Filter.

System and method for controlling a machine, including: receiving a first image processing model trained to classify an input image into a first class for images containing at least one object of a first type or a second class for images not containing an object of the first type; identifying a subset of inference results that are false positive results; generating a set of new training data from the first set of images, including augmenting an image in the first set of images to obtain a respective plurality of images and labeling the respective plurality of images as containing at least one object of a pseudo first class; training a second image processing model to classify an input image into the first class, the second class, and the first pseudo class; and modifying a device setting of a machine based on an inference result of the second image processing model.