Provided is a source-load cooperation access method for a power distribution region. The method includes: establishing a timing feature model of a distributed generator and a timing feature model of a load respectively, acquiring a timing feature of the distributed generator in an access power distribution region by using maximum likelihood estimation and acquiring a timing feature of a user which accesses the power distribution region by using classification and regression trees; and inputting the timing feature of the distributed generator and the timing feature of the user which access the power distribution region into a combination optimization model, and determining a source-load access feeder through optimization.

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.

A self-optimizing controller for equipment of a plant provides a manipulated variable as an input to the plant and receives an output variable as feedback. The controller generates a performance variable model defining the performance variable as a function of the manipulated variable and an output variable model defining the output variable as a function of the manipulated variable. The controller uses the performance variable model to determine a gradient of the performance variable, uses the output variable model to determine a gradient of the output variable, and generates a self-optimizing variable based on the gradient of the performance variable model and the gradient of the output variable model. The controller operates the equipment of the plant to affect a variable state or condition of the building based on the value of the self-optimizing variable from the self-optimizing variable model.

A control device executes a step of starting a computation processing of a prediction model; a step of computing a remaining processing time until the computation processing is completed after starting the computation processing of the prediction model; a step of determining whether the determination of the command value based on an output obtained from the prediction model is made within a control timing for controlling the operation of manufacturing by the manufacturing device, on the basis of a computed remaining processing time; and a step of stopping, when it is determined that the determination of the command value is not made within the control timing, the computation processing of the prediction model, determining the command value on the basis of a value of an intermediate result of the computation processing, and controlling the operation of the manufacturing device on the basis of the determined command value.

A method includes obtaining one or more models associated with a model-based controller in an industrial process having multiple actuator arrays and performing spatial tuning of the controller. The spatial tuning includes identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays. The spatial tuning could also include finding a worst-case cutoff frequency over all output channels for each process input, designing the weighting matrices to penalize higher-frequency actuator variability based on the model(s) and the cutoff frequencies, and finding a multiplier for a spatial frequency weighted actuator variability term in a function that guarantees robust spatial stability. The controller could be configured to use a function during control of the industrial process, where a change to one or more terms of the function alters operation of the controller and the industrial process and at least one term is based on the weighting matrices.

A method for devising an optimum control policy of a controller for controlling a system includes optimizing at least one parameter that characterizes the control policy. A Gaussian process model is used to model expected dynamics of the system. The optimization optimizes a cost function which depends on the control policy and the Gaussian process model with respect to the at least one parameter. The optimization is carried out by evaluating at least one gradient of the cost function with respect to the at least one parameter. For an evaluation of the cost function a temporal evolution of a state of the system is computed using the control policy and the Gaussian process model. The cost function depends on an evaluation of an expectation value of a cost function under a probability density of an augmented state at time steps.

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 self-optimizing controller for equipment of a plant provides a manipulated variable as an input to the plant and receives an output variable as feedback. The controller generates a performance variable model defining the performance variable as a function of the manipulated variable and an output variable model defining the output variable as a function of the manipulated variable. The controller uses the performance variable model to determine a gradient of the performance variable, uses the output variable model to determine a gradient of the output variable, and generates a self-optimizing variable based on the gradient of the performance variable model and the gradient of the output variable model. The controller operates the equipment of the plant to affect a variable state or condition of the building based on the value of the self-optimizing variable from the self-optimizing variable model.

A method for generating closed-loop control parameters of a closed-loop control for a control system of a technical system includes continuous determination of trend data of the closed-loop control during runtime of the technical system by means of the control system, continuous checking of the trend data to determine whether at least one specific trigger criterion has been met, transmitting the trend data of the closed-loop control to a controller optimization module in the event the specific trigger criterion is met, generating revised closed-loop control parameters by the controller optimization module, and transmitting the closed-loop control parameters generated by the controller optimization module to the control system.

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.