Abstract: A system for controlling an operation of an air-conditioning system including a heat exchanger is provided. The system comprises a processor that executes a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger. The historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs. The processor determines a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input and transmits the determined control command to an actuator of the air-conditioning system.

Abstract: The present disclosure provides a system and a method for controlling a multi-mode heat pump having a vapor compression cycle configured to condition a space. The method comprises computing a first first psychrometric variable and a second psychrometric variable based on a current temperature, a setpoint temperature, a current relative humidity, and a setpoint relative humidity of the space, via a psychrometric coordinate transformation. The method further comprises controlling, based on the first psychrometric variable, an expansion valve and a speed of a compressor of the multi-mode heat pump to reduce a magnitude of the first psychrometric variable to a predefined value, and controlling, based on the second psychrometric variable, a speed of at least one of an outdoor fan and an indoor fan of the multi-mode heat pump to reduce a magnitude of the second psychrometric variable to a predefined value.

Abstract: A system controls a vapor compression system containing multicomponent refrigerant mixture by modifying the actuator commands via an output interface, that realizes thermofluid property functions and their derivatives as interpolation functions constructed from anomalous reference thermodynamic property data. The system includes an interface configured to receive measurement data from sensors, a memory configured to store thermofluid property data and computer-executable programs including interpolation functions, and a processor for performing the computer-implemented method. The processor is configured to take as input two thermofluid property variables, and compute using interpolation functions a third thermofluid property variable and its derivatives with respect to input thermofluid property variables.

Abstract: The present disclosure provides a system and a method for controlling a vapor compression system (VCS). The method comprises collecting data points indicative of control of the operation of the VCS with different combinations of setpoints for different actuators of the VCS and corresponding costs of operation of the VCS for each of the different combinations of setpoints, and computing, using a Local Search Region Bayesian optimization (LSR-BO) of the combinations of setpoints and their corresponding costs of operation, a probabilistic surrogate model. The probabilistic surrogate model defines at least first two order moments of the cost of operation. The method further comprises selecting from the probabilistic surrogate model a current combination of setpoints improving the cost of operation with respect to a previous combination of setpoints, according to an acquisition function of the first two order moments of the cost of operation subject to a LRS constraint.

Abstract: The present disclosure discloses a system and a method for controlling an operation of a vapor compression cycle based on a hybrid model of dynamics of the vapor compression cycle including a physics-based model and a data-driven model. The method comprises executing a constrained Kalman smoother over the observed variables collected over multiple instances of time to jointly estimate the parameters of the physics-based model and states of the vapor compression cycle, and updating the data-driven model to minimize a difference between the states estimated by executing the constrained Kalman smoother and the states predicted by the physics-based model. The method further comprises updating the hybrid model with the estimated parameters of the physics-based model and the updated data-driven model, and controlling the operation of the vapor compression cycle using the updated hybrid model.

Abstract: A method and system that describe collecting digital representation of observed variables of the operation of the vapor compression cycle over multiple instances of time and executing a constrained ensemble Kalman smoother for each instance of time to estimate the state variables of the vapor compression cycle for each instance of time. The constrained ensemble Kalman smoother updates the state variables over a sequence of time instances within a smoothing window by solving a series of constrained optimization problems in a range of a covariance, for which constraints are enforced for every variable in the smoothing window for every instance of the constrained optimization problems. The method and system further involve outputting, based on the estimates of the state variables, estimates of variables of the vapor compression cycle at each instance of time.

Abstract: The predictive controller determines, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance, and samples the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon. The predictive controller decodes the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations and controls the mechanical system using a predictive controller that determines control commands changing a state of the operation of the mechanical system using the probability of at least some of the predicted values of the disturbance.

Abstract: To perform a task based on an internal state of a digital twin simulating an operation of a mechanical system, where at least some state variables of the internal states of the digital twin are subject to constraints derived from the physics of a structure of the mechanical system, a processor executes a neural network including an autoencoder trained to process a current internal state of the digital twin and a current control input to the mechanical system to produce a current output of the mechanical system caused by the current control input and an unconstrained next internal state of the digital twin transitioned from the current internal state based on the current control input, and a neural operator trained to modify the unconstrained next internal state of the digital twin to produce a constrained next internal state of the digital twin satisfying the constraints.

Abstract: The present disclosure discloses a device and a method for controlling an operation of a system to perform a task. The system is communicatively coupled to a digital twin configured to concurrently simulate the operation of the system. The method includes collecting a sequence of control inputs for controlling the system to change states of the system according to the task. The method further includes collecting a sequence of outputs of the system caused by the corresponding sequence of control inputs. The method further includes estimating a current internal state of the digital twin using a neural network trained to estimate a sequence of internal states of the digital twin mapping the sequence of control inputs to the sequence of outputs. The method further includes performing the task using the current internal state of the digital twin.

Abstract: The present disclosure discloses a system and a method for controlling an operation of a vapor compression cycle based on a hybrid model of dynamics of the vapor compression cycle including a physics-based model and a data driven model. The method comprises executing a constrained Kalman smoother over the observed variables collected over multiple instances of time to jointly estimate the parameters of the physics-based model and states of the vapor compression cycle, and updating the data driven model to minimize a difference between the states estimated by executing the constrained Kalman smoother and the states predicted by the physics-based model. The method further comprises updating the hybrid model with the estimated parameters of the physics-based model and the updated data driven model, and controlling the operation of the vapor compression cycle using the updated hybrid model.

Abstract: The present disclosure provides a system and a method for controlling a vapor compression system (VCS). The method comprises collecting data points indicative of control of the operation of the VCS with different combinations of setpoints for different actuators of the VCS and corresponding costs of operation of the VCS for each of the different combinations of setpoints, and computing, using a Local Search Region Bayesian optimization (LSR-BO) of the combinations of setpoints and their corresponding costs of operation, a probabilistic surrogate model. The probabilistic surrogate model defines at least first two order moments of the cost of operation. The method further comprises selecting from the probabilistic surrogate model a current combination of setpoints improving the cost of operation with respect to a previous combination of setpoints, according to an acquisition function of the first two order moments of the cost of operation subject to a LRS constraint.

Abstract: A system for controlling an operation of an air-conditioning system including a heat exchanger is provided. The system comprises a processor that executes a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger. The historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs. The processor determines a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input and transmits the determined control command to an actuator of the air-conditioning system.

Abstract: A system for controlling an operation of an air-conditioning system including a heat exchanger is provided. The system comprises a processor that executes a neural network trained to simulate an operation of the heat exchanger for a test control input, to produce an output of the simulation based on historical data defining a state of the heat exchanger. The historical data includes a sequence of historical control inputs provided to the heat exchanger and a sequence of historical outputs of the operation of the heat exchanger corresponding to the sequence of historical control inputs. The processor determines a control command to the air-conditioning system based on the predicted test output of the simulation of the operation of the heat exchanger for the test control input and transmits the determined control command to an actuator of the air-conditioning system.

Abstract: The present disclosure provides a system and a method for monitoring an operation of a vapor compression cycle. The method comprises collecting digital representation of observed variables of the operation of the vapor compression cycle over multiple instances of time and executing a constrained ensemble Kalman smoother for each instance of time to estimate the state variables of the vapor compression cycle for each instance of time. The constrained ensemble Kalman smoother updates the state variables over a sequence of time instances within a smoothing window by solving a series of constrained optimization problems in a range of a covariance, for which constraints are enforced for every variable in the smoothing window for every instance of the constrained optimization problems. The method further comprises outputting, based on the estimates of the state variables, estimates of variables of the vapor compression cycle at each instance of time.

Abstract: A system for controlling or optimizing the performance of a vapor compression system by modifying the actuator commands via an output interface, that realizes thermofluid property functions and their derivatives as spline functions which are represented in a coordinate system that is aligned with a fluid saturation curve. The system includes an interface configured to receive measurement data from sensors, a memory configured to store thermofluid property data and computer-executable programs including a B-spline method, and a processor for performing the computer-implemented method. The processor is configured to take as input two thermofluid property variables, and compute a coordinate transformation in which one axis of the coordinates is aligned with the liquid and vapor saturation curves. In the saturation-curve aligned coordinates, a spline function represents the thermofluid property function, with coefficients and knots stored in memory.

Abstract: A system for designing a circuitry configuration of heat-exchanger units includes an interface to acquire design parameters the heat-exchanger units, a memory to store computer-executable programs including a relaxed decision diagram formation module, and a processor, in connection with the memory, configured to perform the computer-executable programs. The computer-executable programs include steps of providing a configuration of the heat-exchanger units, providing the design parameters of the heat-exchanger units acquired via the interface, generating a relaxed decision diagram based on the design parameters, creating constraints with respect to connections of the heat-exchanger units according to the relaxed decision diagram, and generating feasible configurations of the heat-exchanger units by a mixed-integer-programing method using the constraints.

Abstract: A controller and a method for optimizing a controlled operation of a system performing a task is provided. The method for optimizing the controlled operation of the system comprises accessing a probabilistic distribution of a performance function trained to provide a relationship between different combinations of control parameters for controlling the system and their corresponding costs of operation, selecting a combination of control parameters from the different combinations of control parameters, such that the selected combination of control parameters is having the largest likelihood of being optimal at the probabilistic distribution of the performance function. The method further comprises controlling the system using the selected combination of the control parameters and modifying the probabilistic distribution of the performance function conditioned on the selected combination of the control parameters and the corresponding cost of operation.

Abstract: A calibration system and method for calibrating a model of dynamics of an industrial system is provided. The calibration method includes simulating the model multiple times with different combinations of parameters within an admissible range of values of the parameters to estimate success or failure of the simulation. Training the calibration system, iteratively, until a termination condition is met for defining a likelihood of failure of the simulation of the model and for a probabilistic parameter-to-cost mapping between various combinations of different values of the parameters of the model and their corresponding calibration errors. Further, calibrating, when the termination condition is met, the model with an optimal combination of the parameters having the largest likelihood of minimizing the calibration errors at the probabilistic parameter-to-cost mapping.

Abstract: A controller for controlling a vapor compression system is provided. The controller is configured to control an operation of the VCS with different combinations of setpoints for different actuators of the VCS to estimate a cost of operation of the VCS for each of the different combinations of setpoints, and compute, using a Bayesian optimization of the combinations of setpoints and their corresponding estimated costs of operation, a probabilistic surrogate model, wherein the probabilistic surrogate model defines at least first two order moments of the cost of operation in the probabilistic mapping. The controller is further configured to select an optimal combination of setpoints having the largest likelihood of being a global minimum at the surrogate model according to an acquisition function of the first two order moments of the cost of operation.

Abstract: A system for controlling or optimizing the performance of a vapor compression system by modifying the actuator commands via an output interface, that realizes thermofluid property functions and their derivatives as spline functions which are represented in a coordinate system that is aligned with a fluid saturation curve. The system includes an interface configured to receive measurement data from sensors, a memory configured to store thermofluid property data and computer-executable programs including a B-spline method, and a processor for performing the computer-implemented method. The processor is configured to take as input two thermofluid property variables, and compute a coordinate transformation in which one axis of the coordinates is aligned with the liquid and vapor saturation curves. In the saturation-curve aligned coordinates, a spline function represents the thermofluid property function, with coefficients and knots stored in memory.