A control device to perform feedback control based on a current value, and output a first voltage value includes a feedforward controller using an inverse model of a plant; a feedforward voltage corrector to correct a voltage disturbance due to a modeling error between the plant and a model of the plant; a repetitive controller to learn periodic current disturbances; and a switch. The switch is ON when the current response is in a steady state, and the repetitive controller learns the disturbances and corrects a command current value. A feedback controller outputs the first voltage value by performing the feedback control based on a current value from the corrected command current value, and inputs the command current value to the inverse model to generate a second voltage value. The control device outputs a sum of the first and the second voltage value as a command voltage value.

A dead time estimation device capable of accurately estimating a dead time in a control system is provided. A dead time estimation device 6 includes a dead time calculation section 64 configured to obtain a dead time L{circumflex over ( )}′1 with which an evaluation function J in Equation (1) is at minimum
[Equation 7]
J=∫|Ĝ/e.sup.−{circumflex over (L)}′.sup.1.sup.s−Ĝ′|df  (1)
where G{circumflex over ( )}/e.sup.−L∧′1s is a frequency characteristic of an element from which a dead time element is removed from a transfer function of a control target P and G{circumflex over ( )}′ is a transfer function not including the dead time element in the control target P.

There is a need for solutions for efficiently and reliably maintain data security policies. This need can be addressed by, for example, solutions for performing dynamic security enforcement in a data interaction platform. In one example, a method includes determining a security profile for a data object; receiving a data access request associated with the data object, wherein the data access request is associated with one or more runtime parameters associated with the data access request; determining, based at least in part on the one or more runtime parameters; determining, based at least in part on the selected security environment and the security profile, a selected access level of the plurality of access levels for the data object; and processing the data access request based at least in part on the selected access level.

Centralized controllers for a photovoltaic-enabled distribution grid are described herein. Photovoltaic cells may generate electricity that may be supplied to the distribution grid, which may experience unexpected abnormal power loads. The controllers utilize photovoltaic inverters connected to the photovoltaic cells to optimize the performance of the grid by absorbing or injecting reactive power. These controllers are particularly efficient in handling unexpected abnormal power loads.

It is judged whether a first predicted value of an operation index obtained by inputting a scheduled change value of a manipulation parameter of a plant meets an operation criterion, and whether a second predicted value of the operation index obtained by inputting a virtual change value with a greater change amount from a current value than the scheduled change value to a prediction model meets the operation criterion. If it is judged that the first predicted value and the second predicted value meet the operation criterion, the scheduled change value is output as a command value of the manipulation parameter.

A stochastic model predictive controller (SMPC) estimates a current state of the system and a probability distribution of uncertainty of a parameter of dynamics of the system based on measurements of outputs of the system, and updates a control model of the system including a function of dynamics of the system modeling the uncertainty of the parameter with first and second order moments of the estimated probability distribution of uncertainty of the parameter. The SMPC determines a control input to control the system by optimizing the updated control model of the system at the current state over a prediction horizon and controls the system based on the control input to change the state of the system.

A method of generating, at an IIOT device mounted to equipment of a drilling system, a relay variable, a measurement variable, or a control variable and a message having an IP address. Classifying, by application services of a cloud service provider, the relay variable, the measurement variable, or the control variable; identifying a category or a category and sub-category from a plurality of categories and sub-categories based on variable; cataloguing the relay variable, the measurement variable, or the control variable based on the category or the category and the sub-category; selecting from a library of catalogued relay variables, measurement variables, and control variables, at least one selected from a group comprising a parameter and a value; and identifying a pattern using a statistics based algorithm, the statistics based algorithm using a standard operating procedure, the parameter and the value, the pattern indicating a deviation in the standard operating procedure.

A predictive controller for building equipment associated with a building includes one or more processing circuits configured to control electric energy used by the building equipment. The building equipment includes an electric energy using component. The one or more processing circuits are configured to utilize a predictive cost function to determine a first amount of the electric energy supplied from an energy grid source and a second amount of the electric energy supplied from a second energy source to the electric energy using component.

Methods, devices, and systems related to managing energy using artificial intelligence (AI) are described. In an example, a method can include receiving first signaling including data representing an energy input at a processing resource of a computing device from a radio in communication with a processing resource of an energy source, receiving second signaling including user data at the processing resource of the computing device from a memory of the computing device, inputting the user data and the data representing the energy input into an AI model at the processing resource of the computing device, generating data representing a command as an output of the AI model, and transmitting third signaling including the data representing the command to the processing resource of the energy source from the processing resource of the computing device via the radio in communication with the processing resource of the energy source.

System and method that to shape micro-object density distribution (how densely the micro-objects are assembled in particular spatial regions) are provided. A high speed camera tracks existing object density distribution. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate existing density distribution of objects; receive a desired density distribution of micro-objects; define a model describing a variation of micro-object density over time due to capacitance-based interactions; generate a sequence of electrode potential that when generated would minimize error between the existing density distribution and a desired density distribution; and use the sequences of electrode potentials to actuate the electrodes.