The present disclosure provides a method and an apparatus for adjusting a process control prediction model, and a process controller. In an embodiment, the method includes: determining, based on controlled variable data in process control data obtained through real-time monitoring, whether a prediction performance of the process control prediction model is lower than a reference performance; and when the prediction performance is determined to be lower than the reference performance, using manipulated variable data in the process control data monitored to adjust the process control prediction model. By way of the method, a re-test does not need to be executed to re-identify a model so as to eliminate a mismatch of the process control prediction model, thereby eliminating an influence of fluctuation introduced by addition of an excitation signal during the re-testing.

A controller for controlling a vapor compression system is provided. The controller is configured to control an operation of the VCS with different combinations of setpoints for different actuators of the VCS to estimate a cost of operation of the VCS for each of the different combinations of setpoints, and compute, using a Bayesian optimization of the combinations of setpoints and their corresponding estimated costs of operation, a probabilistic surrogate model, wherein the probabilistic surrogate model defines at least first two order moments of the cost of operation in the probabilistic mapping. The controller is further configured to select an optimal combination of setpoints having the largest likelihood of being a global minimum at the surrogate model according to an acquisition function of the first two order moments of the cost of operation.

A method, system and apparatus for identifying a type of device connected to I/O (Input/Output) channels of an I/O module, can involve applying a current for a predefined time duration to one or more I/O channels of an I/O module, initiating a current readback at a regular interval of time within the predefined time duration, determining values of the current readback for each of a time interval during the predefined time duration, storing the values of the current readback in a memory associated with the I/O module, determining a pattern as a function of the values of the current readback and a sample of time interval for a device connected to the one or more I/O channels, and identifying a type of the device connected to the one or more I/O channels based on a determined pattern of a current sink of the device connected to the one or more I/O channels.

An integrating device 200 for supporting one or more devices 207 capable of delivering mass, energy and/or information to occupants localized to a sub-area of a room is combined with sensors 212 capable of detecting occupants within one sub-area or another and processor 206 which correlates the coordinates of the occupants to those of the delivery devices 207 and a controller 206 that drives the specific sub-area device to provide a localized delivery of services such as lighting, heating, cooling, sound and video, intercom, noise cancellation systems, information, entertainment content, connectivity to the internet or social media, and light therapy in an automatic process based on the detected instantaneous needs of the occupant for services.

A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

This disclosure describes systems, methods, and apparatus for designing and using an H.sub.˜ controller. The controller can be used in an RF power generation system for providing multi-level pulsed waveforms to a plasma load, where robust controller design is desired. This can be achieved through a two phase optimization for the controller based on a first phase of H.sub.∞ loopshaping based on one or more first performance metrics, that acts as a sub-routine within a second phase or larger optimization loop that simulates the controller designed by the H.sub.∞ loopshaping and optimizes based on one or more second performance metrics that may include one or more of the first performance metrics.

Systems include one or more critical datacenter connected to behind-the-meter flexible datacenters. The critical datacenter is powered by grid power and not necessarily collocated with the flexboxes, which are powered “behind the meter.” When a computational operation to be performed at the critical datacenter is identified and determined that it can be performed at a lower cost at a flexible datacenter, the computational operation is instead routed to the flexible datacenters for performance. The critical datacenter and flexible datacenters preferably shared a dedicated communication pathway to enable high-bandwidth, low-latency, secure data transmissions.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to generate an event subscription for a consuming system, the event subscription defining events to be sent to the consuming system. The building system operates to receive an event from an event source, the event comprising data and a timestamp and identify contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building. The building system operates to enrich the event with the contextual data and provide, based on the event subscription and the contextual data of the enriched event, the enriched event to the consuming system.

A control system for an operable system such as a flow control system or temperature control system. The system operates in a control loop to regularly update a model with respect at least one optimizable input variable based on the detected variables. The model provides prediction of use of the input variables in all possible operation points or paths of the system variables which achieve an output setpoint. In some example embodiments, the control loop is performed during initial setup and subsequent operation of the one or more operable elements in the operable system. The control system is self-learning in that at least some of the initial and subsequent parameters of the system are determined automatically during runtime.

Systems and methods for providing visualization of health risks within a building. Health risk levels for building spaces are determined using occupancy data and health risk data relating to a risk of contracting or spreading an infectious disease. A visualization of the health risk levels is generated and presented on a user interface.