Provided is a control device for performing model prediction control. A position of a virtual obstacle associated with a real obstacle is set based on a position of the real obstacle acquired by a first acquisition part so that the virtual obstacle is positioned substantially symmetrically to the real obstacle with reference to a following target trajectory of a control target. Stage costs calculated by a prescribed evaluation function include: a first stage cost associated with a first probability potential field representing a probability that the real obstacle is present based on the position of the real obstacle; and a second stage cost associated with a second probability potential field representing a probability that the virtual obstacle is present based on the position of the virtual obstacle and having a probability value equal to or greater than that of the first probability potential field.

A process control system for controlling a process including a plurality of sub-processes, the process control system including a plurality of control modules each associated with one of the plurality of sub-processes. At least one of the plurality of control modules includes a model, a communicator, and a controller. The model includes a sub-process model defining a relationship between variables of the associated sub-process, and an inter-sub-process model defining a relationship between a variable of another sub-process and at least one of the variables of the associated sub-process. The communicator communicates with control module associated with the another sub-process to determine an updated value for the variable of the another sub-process. The controller uses the model and the updated value to determine a control signal for adjusting a manipulated variable of the associated sub-process. The process control method is also provided that is performed by the process control system.

A method of generating a hybrid model predictive control (MPC) simulation model for a plant configured to run a process that processes at least one raw material to generate at least one tangible product. A predictive dynamic MPC sub-model is provided for each of plurality of process units in the plant, the plant including at least one process controller coupled to field devices coupled to the plurality of process units, where the process units comprise equipment for converting the raw material or an intermediate material formed from the raw material into to another material. A piping network diagram is obtained that provides a representation of a piping network for routing of the raw material and the intermediate material during the process. The dynamic MPC sub-models are coupled together using the piping network to generate the hybrid MPC simulation model which models the plant as a whole.

A metrology method for use in determining one or more parameters of a patterned structure, the method including providing raw measured TEM image data, TEM.sub.meas, data indicative of a TEM measurement mode, and predetermined simulated TEM image data including data indicative of one or more simulated TEM images of a structure similar to the patterned structure under measurements and a simulated weight map including weights assigned to different regions in the simulated TEM image corresponding to different features of the patterned structure, performing a fitting procedure between the raw measured TEM image data and the predetermined simulated TEM image data and determining one or more parameters of the structure from the simulated TEM image data corresponding to a best fit condition.

A second-life battery-based super multi-objective energy management method for a smart community microgrid, including: establishing a residual life decay model of the second-life battery based on a residual charge and discharge cycle number; establishing a super multi-objective energy management model based on residential energy consumption costs, the residual life decay model of the second-life battery, residential electricity consumption behaviors, and impacts of residential community load on an electricity distribution system; recording state information of the second-life battery in the smart community; collecting residential electricity consumption information in the smart community, and predicting a renewable energy output value of the smart community; and solving the super multi-objective energy management model by using a NSGA-III algorithm combining with the state information of the second-life battery and the renewable energy output value.

A semiconductor device is provided. The semiconductor device includes a processing device that provides resource usage information including a utilization value; and a prediction information generating device that generates resource usage prediction information based on the resource usage information and provides the resource usage prediction information to the processing device. The prediction information generating device includes: an error calculator to calculate an error value between the utilization value and a predicted value included in the resource usage prediction information; a margin value calculator to receive the error value from the error calculator and calculate a margin value using the error value; an anchor value calculator to calculate an anchor value using the utilization value; and a predictor to output the predicted value using the anchor value and the margin value. The processing device controls resource allocation of the processing device based on the resource usage prediction information.

The disclosed systems and techniques enable dual mode operation for model-based controllers in which the controllers are capable of operating in both (i) a constrained solution mode, and (ii) an unconstrained solution mode. The dual mode operation improves control because it enables the use of constrained solution mode operation when possible (constrained solution mode often enables superior control) and enables the use of unconstrained solution mode when constrained solution mode is not possible (e.g., when it is impossible to develop the constrained solution with the time available). This enables superior control when compared to typical model predictive control (MPC) controllers.

An industrial asset may have monitoring nodes (e.g., sensor or actuator nodes) that generate current monitoring node values. An abnormality detection and localization computer may receive the series of current monitoring node values and output an indication of at least one abnormal monitoring node that is currently being attacked or experiencing a fault. An actor-critic platform may tune a dynamic, resilient state estimator for a sensor node and output tuning parameters for a controller that improve operation of the industrial asset during the current attack or fault. The actor-critic platform may include, for example, a dynamic, resilient state estimator, an actor model, and a critic model. According to some embodiments, a value function of the critic model is updated for each action of the actor model and each action of the actor model is evaluated by the critic model to update a policy of the actor-critic platform.

New and/or alternative approaches to engine performance control that can account for the need to robustly monitor performance and/or operation of the physical plant and actuators thereof, while avoiding or limiting performance degradation. Model predictive control (MPC) or other control configuration such as proportional-integral-derivative control may be used to control the system by identifying a performance optimized control solution. In some examples, a modification to the performance optimized solution analysis is made to weight control solutions in favor of robust monitoring conditions. In other examples, the performance optimized solution is post-processed and modified to favor robust monitoring conditions.

There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.