A control system for a plant includes a controller and a sensor. The controller is configured to estimate a response time of the plant and adjust a sampling rate based on the estimated response time. The response time is a parameter that characterizes a response of the plant to a disturbance. The sensor is configured to receive the adjusted sampling rate from the controller, collect samples of a measured variable from the plant at the adjusted sampling rate, and provide the samples of the measured variable to the controller.

The embodiments described herein include one embodiment that a control method including executing an infeasible search algorithm during a first portion of a predetermined sample period to search for a feasible control trajectory of a plurality of variables of a controlled process, executing a feasible search algorithm during a second portion of the predetermined sample period to determine the feasible control trajectory if the infeasible search algorithm does not determine a feasible control trajectory, and controlling the controlled process by application of the feasible control trajectory.

A control system for a plant includes a controller configured to detect a disturbance in the control system. In response to detecting the disturbance, the controller is configured to evaluate a signal affected by the disturbance to estimate a response time of a plant. The response time is a parameter that characterizes a response of the plant to the disturbance. The controller is configured to adjust an operating parameter used by the control system based on the estimated response time. The controller is configured to use the adjusted operating parameter to generate and provide an input to the plant.

A building system of a building including one or more memory devices having instructions thereon, that, when executed by one or more processors, cause the one or more processors to manage a plurality of entitlements for a plurality of subscriptions of one or more buildings with a building entitlement model, receive a first request to perform a first operation for a first subscription and a second request to perform a second operation for a second subscription, and implement the first operation on first computing resources of a first zone based on the building entitlement model in response to a first determination that the first subscription has the first entitlement and implement the second operation on second computing resources of the second zone based on the building entitlement model in response to a second determination that the second subscription has the second entitlement.

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 machine learning device learns an action of a driving source in a transport device continuously transporting at least two transported objects along a transport path, and includes: a hardware processor that: acquires position information of the at least two transported objects on the transport path on the basis of a result of detection by a sensor provided in the transport path; calculates a reward on the basis of the position information acquired, according to a predetermined rule; learns an action by calculating an action value in reinforcement learning on the basis of the position information acquired and the reward calculated; and generates and outputs control information that causes the driving source to perform an action determined on the basis of a learning result.

Provided is an aluminum oxide production operation optimization system and method based on a cloud-edge collaboration, which relates to the technical field of an aluminum oxide production operation optimization. According to the system and method, firstly the whole-flow data in the aluminum oxide production process is acquired, the data is pre-processed, then the pre-processed data is transmitted to a local collaboration production operation optimization unit, the local collaboration production operation optimization unit firstly judges working conditions for the current aluminum oxide production process, an optimization strategy needing to be operated at present is automatically switched according to the working condition, and the local operation optimization strategy obtains the actual setting value of the aluminum oxide production operation indexes.

Production tasks are scheduled to optimize production constraints while taking time varying energy prices into account. An energy management layer (EML) is in communication with a manufacturing domain as well as a smart grid domain. The EML receives information relating to a factory workflow, costs associated with operating units within the factory, and static utility information such as tariff models and incentives. Time variant energy price information is obtained from a utility regarding periodic price levels for a near future timeframe. The workflow is converted to a matrix representation that is acceptable to a mixed integer linear programming (MILP) solver. An algebraic factor representing a tradeoff between energy cost and full-capacity production is minimized to produce production variables which control production and limit production units and tasks to be performed during time slots that are most economically favorable.

The controlling method uses a control specification. In the method, at least part of the progression of the controlling process is monitored, and at least one quality criterion characterizing the quality of the control method is determined, (e.g., ascertained), in accordance with the progression. The control specification is adjusted in accordance with the quality criterion.

According to an exemplary embodiment of the present disclosure, an optimal control method performed by a computing device including at least one processor is disclosed. The method includes acquiring state information including at least one state variable; calculating first control information by inputting the state information to a reinforcement learning control model; calculating second control information from the state information based on a feedback control algorithm; and calculating optimal control information based on the first control information and the second control information.