A model-based control system is configured to select a desired parameter of a machinery configured to produce power and to output emissions and to select an emissions model configured to use the desired parameter as input and to output an emissions parameter. The model-based control system is additionally configured to tune the emissions model via a tuning system to derive a polynomial setpoint, and to control one or more actuators coupled to the machinery based on the polynomial setpoint.

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.

A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.

Systems and methods for estimating a hardness of a rock mass during operation of an industrial machine. One system includes an electronic processor configured to receive a rock mass model and to receive live drilling data from the industrial machine. The electronic processor is also configured to update the rock mass model based on the live drilling data and to estimate a drilling index for a hole based on the updated rock mass model. After estimating a drilling index for the hole, the electronic processor is also configured to set a blasting parameter for the hole based on the estimated drilling index.

Processes and systems for controlling operation of a commercial refinery distillation column and/or splitter operable to separate hydrocarbons. An automated process controller (APC) receives signal from at least one analyzer that provides information about the concentration of at least a first chemical in a first fraction and a second chemical in a second fraction obtained from the distillation column. The APC comprises programming in the form of an algorithm that calculates real-time monetary values for the first chemical and the second chemical and alters the operation of the distillation column to change either the percentage of the first chemical in the second fraction or the percentage of the second chemical in the first fraction, thereby maximizing overall operational profit for the distillation column.

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

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.

A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.

A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

An HVAC system includes a compressor, condenser, and evaporator. A sensor measures a value associated with the refrigerant in the condenser or the evaporator, and a controller is communicatively coupled to the compressor and the sensor. The controller determines, based on an operational history the compressor, that pre-requisite criteria are satisfied for entering a sensor validation mode. After determining the pre-requisite criteria are satisfied, an initial sensor measurement value is determined. Following determining the initial sensor measurement value, the compressor is operated according to a sensor-validation mode. Following operating the compressor according to the sensor-validation mode for at least a minimum time, a current sensor measurement value is determined. The controller determines whether validation criteria are satisfied for the current sensor value. In response to determining that the validation criteria are satisfied, the controller determines that the sensor is validated.