A control apparatus includes a prediction unit configured to repeatedly predict a first target value based on prediction information; a transmission/reception unit configured to repeatedly transmit the prediction information to a server and receive a second target value having higher prediction accuracy than the first target value predicted by the server; a management unit configured to update a first error of prediction in the prediction unit based on the second target value and the first target value; and a setting unit configured to set a control target value based on the first target value and the first error. A first time interval in which the prediction unit repeatedly predicts the first target value is shorter than a second time interval in which the transmission/reception unit repeatedly transmits the prediction information to the server.

A method for controlling an industrial process includes: determining, by a process controller, based at least in part on a set of current values and/or past values of state variables of the industrial process, a set of control outputs to be applied to at least one actor and/or lower-level controller configured to cause a performing of at least one physical action on the process; querying, based on at least a subset of the set of current values and/or past values of state variables and on at least a subset of the set of control outputs, a trained machine-learning model configured to output a classification value, and/or a regression value, that is indicative of a propensity of a watching human operator to at least partially override the control outputs delivered by the process controller; and determining that the classification value, the regression value, and/or the propensity, meets a predetermined criterion.

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

Systems and methods are configured to control operation of an HVAC system providing climate control for a zone of a structure. In various embodiments, a constrained optimization problem is performed to set control commands for controlling operation of the HVAC system to achieve one or more objectives while providing a supply air flow at an air temperature and a humidity ratio for the zone at a future time. For instance, the optimization problem may include a cost function having constraints based on a desired temperature setpoint and a humidity ratio for the zone. A value is set for each control command based on the performance of the constrained optimization problem to achieve at least one of the objectives and as a result, the supply air flow at the air temperature and humidity ratio is provided by the HVAC system at the future time to the zone based on the values.

A system for controlling an operation of a machine for performing a task is disclosed. The system submits a sequence of control inputs to the machine and receives a feedback signal. The system further determines, at each control step, a current control input for controlling the machine based on the feedback signal including a current measurement of a current state of the system by applying a control policy transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of a feedback controller. Furthermore, the system may iteratively update a state of the feedback controller defined by the control parameters using a prediction model predicting values of the control parameters and a measurement model updating the predicted values to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective.

A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.

The present disclosure describes system and methods for inferring energy usage at multiple levels of granularity. One embodiment describes an industrial automation system including a first industrial automation component, a first sensor coupled to the first industrial automation component, in which the first sensor measures a first amount of power supplied to the first industrial automation component, a second industrial automation component that couples to the first industrial automation component, and an industrial control system that infers energy usage by the first industrial automation component and the second industrial automation component based at least in part on the first amount of power supplied to the first industrial automation component.

A building management system includes a building efficiency improvement system and method configured to monitor and control subsystems and equipment for improved efficiency of operation. A user device is configured to display a user interface for monitoring and controlling one or more building equipment efficiency parameters and settings. The building efficiency management system further includes a controller configured to collect and analyze data from equipment, generate displays of the operational status and efficiency levels, generate sets of alternative equipment control algorithms based on efficiency objectives, and present users with a set of alternative equipment control algorithms displayed via graphic user interface elements on the user device. The user device further provides a means to select and implement an alternate equipment control algorithm. The controller is further configured to receive inputs from the user device commanding changes to equipment controls and process transactions associated with changes to equipment configuration.

A model-based control system is configured to select a desired parameter of a machinery configured to produce power and to output emissions and to select an emissions model configured to use the desired parameter as input and to output an emissions parameter. The model-based control system is additionally configured to tune the emissions model via a tuning system to derive a polynomial setpoint, and to control one or more actuators coupled to the machinery based on the polynomial setpoint.