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.

An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

A control system for controlling an operation of a machine subject to constraints including equality and inequality constraints on state and control variables of the system iteratively solves an optimal control structured optimization problem (OCP), such that each iteration outputs primal variables and dual variables with respect to the equality constraints and dual variables and slack variables with respect to the inequality constraints. For a current iteration, the system classifies each of the inequality constraints as an active, an inactive or an undecided constraint based on a ratio of a slack variable to a dual variable of the corresponding inequality constraint determined by a previous iteration, finds an approximate solution to the set of relaxed optimality conditions subject to the equality constraints and the active and undecided inequality constraints, and update the primal, dual, and slack variables for each of the equality and inequality constraint.

A production system includes an information processing device that carries out the processes of: (i) generating one or more production condition candidates each of which is a candidate for a production condition under which the product is produced; (ii) determining, using a prediction model, a prediction of a production result of a case in which the product is produced under each of the one or more production condition candidates; and (iii) generates, by evaluating a result of the prediction based on a predetermined evaluation standard, an evaluation of each of the one or more production condition candidates. The information processing device repeats the process (ii) while changing between the one or more production condition candidates, and determines a production condition candidate, the evaluation of which in the process (iii) satisfies a predetermined standard, to be the production condition under which the product is produced.

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.

A method for controlling an electric drive system and the electric drive system. The method includes: measuring an external variable; estimating a control variable for a current sampling step with a mathematical model; predicting a control variable for a future sampling step for each of a plurality of candidate voltage vectors selected for the future sampling step; and calculating a cost function, and identifying a primary voltage vector giving a minimum, where the cost function is defined as a deviation between the predicted stator flux and the reference stator flux. The method further includes: predefining a lookup table giving a correlation between a nonzero voltage vector and a voltage vector group including four candidate voltage vectors, where the plurality of candidate voltage vectors is selected referring the lookup table. The electric drive system includes motor, power converter, and controller, and configured to perform the method.

A system for predicting a negative pressure of a brake booster of a vehicle includes: a driving information detector configured to detect driving information according to driving of the vehicle; and a controller configured to calculate a negative pressure of an intake manifold based on a pressure of the intake manifold and an atmospheric pressure that is the driving information and including a booster negative pressure predictor configured to predict the negative pressure of the brake booster by integrating over time a change rate according to a charging rate and a discharging rate of the negative pressure calculated using a previous negative pressure of the brake booster calculated in a previous cycle according to a logic for predicting the negative pressure of the brake booster and the negative pressure of the intake manifold and a brake pedal force of a current cycle.

A system for predicting a negative pressure of a brake booster of a vehicle includes: a driving information detector detecting driving information related to driving of the vehicle; and a controller determining a negative pressure of an intake manifold based on a pressure of the intake manifold and an atmospheric pressure which is the driving information and including a booster negative pressure predictor predicting the negative pressure of the brake booster by integrating over time a change rate according to a charging rate and a discharging rate of the negative pressure determined using a negative pressure of the brake booster determined in a previous cycle according to a logic for predicting the negative pressure of the brake booster and the negative pressure of the intake manifold of a current cycle and an imitated brake pedal force signal of the current cycle imitating an acceleration of the vehicle.

A power system energy management system with dynamic state estimation (DSE) is disclosed wherein system dynamic states are estimated using SCADA measurements, PMU measurements, signals of controllers, digital recorders, protection devices, and smart electronic devices. The DSE is solved first by Unscented Kalman Filter, and if the Unscented Kalman Filter is failed, weighted lease square is used to solve the DSE. If weighted lease square is failed, integration method is used to calculate the dynamic states. In another aspect, Unscented Kalman Filter, weighted lease square, and integration calculation are applied to solve the DSE by nodal parallel computing for each generation system.

System and method for controlling a machine, including: receiving a first image processing model trained to classify an input image into a first class for images containing at least one object of a first type or a second class for images not containing an object of the first type; identifying a subset of inference results that are false positive results; generating a set of new training data from the first set of images, including augmenting an image in the first set of images to obtain a respective plurality of images and labeling the respective plurality of images as containing at least one object of a pseudo first class; training a second image processing model to classify an input image into the first class, the second class, and the first pseudo class; and modifying a device setting of a machine based on an inference result of the second image processing model.