The present invention provides a control device that executes model predictive control related to a predetermined control target corresponding to an actual target device which is an actual target of servo control in order to cause an output of the actual target device to track a predetermined command. The control device includes: an integrator which receives input of a deviation between the predetermined command and an output of the predetermined control target; and a model predictive control unit which has a prediction model that defines a correlation between a predetermined state variable and an input to the predetermined control target in a form of a predetermined state equation, and which performs model predictive control based on the prediction model according to a predetermined evaluation function in a prediction section having a predetermined time width using the output of the integrator as an input.

A method of generating a controller for a continuous process. The method includes receiving from a storage memory, off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.

A controller for HVAC equipment of a plant includes a processing circuit configured to predict an impact of a time delay of the plant on a performance variable received as feedback from the plant. The processing circuit is configured to artificially increase or decrease a value of the performance variable using an adjustable time delay parameter to at least partially negate the impact of the time delay on the performance variable. The processing circuit is configured to use the artificially increased or decreased value of the performance variable in on-off feedback control to operate the HVAC equipment.

According to some aspects of the disclosure, a computer-implemented method, a computer program and a process control device for selecting at least one set of target cells from multiple sets of candidate cells are provided. The method can include receiving data collected from a plurality of processes, wherein each of the processes produces a distinct set of candidate cells. The method further comprises the received data including values of process outputs being a product quality attribute or a key performance indicator for selecting the target cells.

This disclosure describes techniques that enable a supplemental voltage to be delivered to a Remote Radio Unit (RRU) to compensate for a voltage loss that occurs between a primary Direct Current (DC) power source and the RRU. A supplemental voltage controller is described that captures current environmental metadata associated with an operation of the RRU and calculates an RRU voltage at the RRU. In doing so, the supplemental voltage controller may generate a supplemental voltage control signal that supplements the RRU with a supplemental DC voltage.

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

A hierarchical control method for an island power grid energy storage system for increasing new energy generation fluctuation is disclosed. The method includes implementing a rolling dispatch method based on an idea of model predictive control; comprehensively considering a response capability of the energy storage system to grid-wide economic dispatch instructions and life loss of energy storage lithium batteries, and constructing an objective function of an intraday control model of the energy storage system with economic optimization; and constructing, on a basis of satisfying the objective function of the intraday control model, a real-time control model of the energy storage system and solving the model, and determining charge and discharge plans of a lithium battery energy storage system and a supercapacitor energy storage system.

The invention relates, amongst other things, to a method for operating a client device. If a first operation command is not received from a server device within a predetermined period of time in response to sending at least one first sensor signal, one of a plurality of predictions is selected depending on at least one first sensor signal and/or at least one currently detected sensor signal and a plurality of sensor signal hypotheses and the client device is operated with at least one operation command hypothesis of the selected prediction. The invention also relates, amongst other things, to a method for operating a server device.

A predictive controller controls a system under uncertainty subject to constraints on state and control variables of the system. At each control step, the predictive controller solves an inequality constrained nonlinear dynamic optimization problem including probabilistic chance constraints representing the uncertainty to produce a control command, and controls an operation of the system using the control command. The predictive controller solves the dynamic optimization problem based on a two-level optimization that alternates, until a termination condition is met, propagation of covariance matrices of the probabilistic chance constraints within the prediction horizon for fixed values of the state and control variables with optimization of the state and control variables within the prediction horizon for fixed values of the covariance matrices.

Systems and methods are configured to managing and controlling an energy system comprising at least one primary energy load, at least one secondary energy load, and an energy storage subsystem configured to receive energy from an energy source and provide energy to the energy loads. In embodiments, a method is provided. The method includes receiving a first system state data object associated with a first timepoint, generating a second system state data object associated with a second timepoint, determining control commands for each of the at least one primary energy load, the at least one secondary energy load, and the energy storage subsystem based at least in part on evaluating the second system state data object in a constrained optimization model, and causing energy to be supplied at the second timepoint to the energy loads and/or the energy storage subsystem based at least in part on the control commands.