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.

An artificial-intelligence system for manipulating resources to minimize exposure harm in a chaotic environment, comprising autonomous agent devices, remote electronic sensors, and a central server. The central server receives a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording values of one or more variables in the chaotic environment; receives a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum permitted risk exposure for the time interval; determines a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment; determines that the weighted total risk exposure exceeds the maximum permitted risk exposure; and causes the autonomous agent devices to manipulate the one or more resources to decrease the weighted total risk exposure.

A system that is configured to automatically control a spooling of a drum of a deployment unit used in hydrocarbon recovery operations.

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.

A method for controlling sub-acute ruminal acidosis (SARA) in a dairy cow. The method includes placing a pH sensor in a rumen of the dairy cow, obtaining pH data of the rumen utilizing the pH sensor, receiving the pH data by one or more processors, calculating a first average of the pH data and a second average of the pH data utilizing the one or more processors, generating a plurality of pH classes based on a plurality of predefined pH ranges, generating a plurality of sodium bicarbonate classes based on a plurality of predefined sodium bicarbonate mass ranges, designing a fuzzy control system based on a set of rules, and determining an amount of sodium bicarbonate to add to a water supply of the dairy cow utilizing the fuzzy control system based on the first average and the second average.

A correction control method includes steps of rebuilding a fuzzy inference system, setting a maximum correction value and a minimum correction value, determining whether there is an output value to be outputted, deciding a intermediate correction value and determining whether a range of an interval between the maximum correction value and the intermediate correction value is enough to constitute the fuzzy interval of the membership function, allowing the correction unit to output the maximum correction value, the minimum correction value and the intermediate correction value, adjusting the fuzzy inference system, allowing the fuzzification unit to constitute the minimum correction value according to the output value, determining whether a range of an interval between the maximum correction value and the minimum correction value is enough to constitute the fuzzy interval, and notifying that functions of the fuzzy inference system are failed. The functionality of supervised algorithm of hidden switch is enhanced.

Graphical element search technique selection, fuzzy logic selection for anchors and targets, and hierarchical graphical element identification for robotic process automation (RPA) are disclosed. The fuzzy logic selection of anchors and targets may be part of a larger, tiered, or hierarchical process for identifying graphical elements in the UI. When a selector for a UI element is not found with at least a confidence threshold, similar elements potentially corresponding to the selector for a UI element target may be searched based on fuzzy matching of the target and corresponding anchor(s). Geometric matching may also be employed between the target UI element and its respective anchor(s). The combination of fuzzy matching and geometric matching may allow for more flexible and accurate identification of the exact selector with which an RPA robot is attempting to interact.

An industrial development hub (IDH) supports industrial development and testing capabilities that are offered as a cloud-based service. The IDH comprises an enhanced storage platform and associated design tools that serve as a repository on which customers can store control project code, device configurations, and other digital aspects of an industrial automation project. The IDH system can facilitate discovery and management of digital content associated with control systems, and can be used for system backup and restore, code conversion, and version management.

An action control method and apparatus related to the field of artificial intelligence, where the method includes obtaining states of N dimensions of an artificial intelligence device, obtaining a plurality of discrete decisions based on an active fuzzy subset and a control model that are of a state of each of the N dimensions, where an active fuzzy subset of a state is a fuzzy subset whose membership degree of the state is not zero, the membership degree is used to indicate a degree that the state belongs to the fuzzy subset, performing, based on a membership degree between a state and an active fuzzy subset that are of each dimension, weighted summation on the plurality of discrete decisions, to obtain a continuous decision, and controlling, based on the continuous decision, the artificial intelligence device to execute a corresponding action.

An industrial development hub (IDH) supports industrial development and testing capabilities that are offered as a cloud-based service. The IDH comprises an enhanced storage platform and associated design tools that serve as a repository on which customers can store control project code, device configurations, and other digital aspects of an industrial automation project. The IDH system can facilitate discovery and management of digital content associated with control systems, and can be used for system backup and restore, code conversion, and version management.