Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated. Generating the optimal control configuration comprising the optimal control variable values associated with the continuous flow process is based on solving a deterministic convex optimization problem and a corresponding stochastic optimization problem.

A method includes simulating one or more states of a movable object by implementing a movable object model. Each simulated state is associated with simulated state data of the movable object. The method further includes determining one or more sets of simulated sensor data corresponding to the one or more simulated states respectively by implementing a plurality of sensor models, determining environment data of a simulated environment surrounding the movable object by implementing an environment model, providing the one or more sets of simulated sensor data to a movable object controller configured for generating control signals to adjust states of the movable object, and providing the simulated state data and the environment data to a vision simulator configured for visualizing operations of the movable object in the one or more simulated states.

To facilitate evaluation of a predicted process shape in process recipe development using machine learning, a process recipe search apparatus that searches for an etching recipe that is a parameter of a plasma processing apparatus set so as to etch a sample into a desired shape displays, on a display device, the predicted process shape of the sample by a candidate etching recipe predicted by using a machine leaning model, by highlighting a difference between the predicted process shape and a target shape.

A system for controlling an operation of a machine for performing a task is disclosed. The system submits a sequence of control inputs to the machine and receives a feedback signal. The system further determines, at each control step, a current control input for controlling the machine based on the feedback signal including a current measurement of a current state of the system by applying a control policy transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of a feedback controller. Furthermore, the system may iteratively update a state of the feedback controller defined by the control parameters using a prediction model predicting values of the control parameters and a measurement model updating the predicted values to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective.

A system for flight control system using simulator data for an electric aircraft is presented. The system includes a computing device, the computing device configured to receive a plurality of measured flight data, simulate a plurality of aircraft performance model outputs as a function of a flight simulator and the plurality of measured flight data, determine a moment datum as a function of the plurality of measured flight data and the plurality of aircraft performance model outputs, generate an allocation command datum as a function of the moment datum and the plurality of aircraft performance model outputs, and perform a torque allocation on a flight component of a plurality of flight components as a function of the allocation command and the moment datum.

The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).

A servo control device includes a velocity command creation part configured to create a velocity command value for driving a servomotor; a velocity detection part configured to detect velocity of the servomotor; and a torque command creation part configured to create a torque command value using a difference between the velocity command value and the velocity detection value. The torque command creation part has an integral gain part and a proportional gain part, an integral gain and a proportional gain are obtained by multiplying a value calculated by multiplying an initial value by a ratio of load inertia of a machine relative to rotor inertia of the servomotor, by an integral gain magnification and a proportional gain magnification, respectively, and the integral gain magnification is set to a value smaller than the square of the proportional gain magnification according to a delay time of a velocity control loop.

The subject matter of this specification can be embodied in, among other things, a method for time shifting when a cold storage facility is cooled that includes determining a thermal model of a cold storage facility, obtaining an energy cost model that describes a schedule of variable energy costs over a predetermined period of time in the future, determining an operational schedule for at least a portion of a refrigeration system based on the thermal model, the energy cost model, and a maximum allowed temperature, and powering on the portion the refrigeration system based on the operational schedule, cooling, by the powered portion of the refrigeration system to a temperature below the maximum allowed temperature, reducing power usage of the powered portion of the refrigeration system based on the operational schedule, and permitting the facility to be warmed by ambient temperatures toward the maximum allowed temperature.

According to one embodiment, a data collection system includes an event data collector, a state machine generator, a state machine list, and a state machine driver. The event data collector collects sense signals respectively as a plurality of event data. The state machine generator generates a state machine as a model corresponding to the workpiece. One of the sense signals is acquired when the workpiece is fed into the processing system. The state machine generator generates the state machine and generates an ID for the state machine when the event data collector collects one of the plurality of event data corresponding to the one of the sense signals. The state machine driver drives the state machine retained in the state machine list by sending, to the state machine retained in the state machine list, an event corresponding to another one of the sense signals.