Abstract: A controller uses a motion trajectory for controlling a motion of a device to perform a task subject to constraints. The controller evaluates a parametric function to output predicted values for a set of discrete variables in a mixed-integer convex programming (MICP) problem for performing the task defined by the parameters. The controller fixes a first subset of discrete variables in the MICP to the predicted values outputted by the trained parametric function and updates at least some of the predicted values of a remaining subset of discrete variables to values are uniquely defined by the fixed values for the first subset of discrete variables and the constraints. Hence, the controller transforms the MICP into a convex programming (CP) problem, solves the CP problem subject to the constraints to produce a feasible motion trajectory, and controls the device according to the motion trajectory.

Abstract: A method and a system for controlling an industrial machine generate multiple outcomes by optimizing an acausal coactive acquisition function configured to maximize a joint gain in the utility of the generated outcomes according to the trained utility model and a preference of the generated outcomes according to the trained preference outcome model. In response to determining a desired outcome based on the generated outcomes, the system determines the operating parameters of the industrial machine corresponding to the desired outcome; and controls the industrial machine according to the mapped operating parameters.

Abstract: The present disclosure provides a feedback control system for optimizing performance of a grid-interactive building (GIB) system that is configured to condition an indoor environment of a building. The feedback control system comprises a time-series foundation model configured to predict disturbances affecting energy consumption of the building; and a stochastic feedback controller configured to: determine control inputs for the GIB system by evaluating multiple control actions for the GIB system based on the predicted disturbances, wherein the control inputs maximize a likelihood of achieving desired indoor environmental conditions while minimizing the energy consumption.

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 motion of a device from an initial state to a target state in an environment having obstacles that form constraints on the motion of the device. The method includes executing a learned function trained with machine learning to generate a feasible or infeasible trajectory connecting the initial state of the device with the target state of the device while penalizing an extent of violation of at least some of the constraints to produce an initial trajectory. The method further includes solving a convex optimization problem subject to the constraints to produce an optimal trajectory that minimizes deviation from the initial trajectory and controlling the motion of the device according to the optimal trajectory.

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 an aircraft within a terminal maneuvering area (TMA) of an airport in the presence of multiple other aircraft. The method includes solving an optimal control problem subject to constraints maintaining a pre-determined separation of the aircraft from the other aircraft in the TMA to determine a state trajectory of the aircraft indexed on a predetermined sequence of TMA stages of the aircraft approaching a merging point in the TMA. The state trajectory of the aircraft is a sequence of states having a one-to-one correspondence with the sequence of TMA stages. The state of the aircraft includes a time state variable indicative of a time remaining for reaching the merging point. The aircraft is then controlled on the basis of an optimal state trajectory that is determined using the state trajectory.

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 multi-split type refrigeration cycle apparatus includes a refrigerant circuit including a plurality of expansion valves and a controller to set opening command values for the plurality of expansion valves. The controller sets target openings of the plurality of expansion valves to a plurality of provisional openings, respectively, in an initial step, and determines whether the plurality of provisional openings satisfy an inequality constraint in a first step.

Abstract: The present disclosure provides a feedback controller and method for controlling an operation of a device at different control steps. The feedback controller comprises at least one processor, and the memory having instructions stored thereon that, when executed by the at least one processor, causes the feedback controller, for a control step, to collect a measurement indicative of a state of the device at the control step, and execute, recursively until a termination condition is met, a probabilistic solver parameterized on a control input to an actuator operating the device to produce a control input for the control step. The feedback controller is further configured to control the actuator operating the device based on the produced control input.

Abstract: A method for controlling a system by a controller comprises accepting a current state of the system and selecting, using a trained function of the current state, a solver from a set of solvers. The method further comprises solving an optimal control optimization problem using the selected solver to produce a current control input, such that for at least some different control steps, the predictive controller solves a formulation of the optimal control optimization problem with different solvers having different accuracies, requiring different computational resources, or both and submitting the current control input to the system thereby changing the current state of the system.

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: A controller is provided for operating a system under admissible states. The controller includes an interface configured to connect the system storing a set of measured system states, a set of reference inputs and a set of system parameters in a storage arranged inside or outside the system, a memory storing measured system states, admissible reference inputs and admissible parameter sets and computer-executable programs including a parameter estimator and an adaptive reference governor (ARG), a processor, in connection with the memory. The processor is configured to perform the ARG and the parameter estimator. The parameter estimator extracts a pair of a reference input and the system state and compute a system parameter estimate based on the reference input and system state.

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: A controller is provided for generating a policy controlling a system by learning a dynamics of the system.

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 controller for controlling a motion of at least one device subject to constraints on the motion, is disclosed. The controller comprises a processor and a memory, where the controller inputs parameters of the task including the state of the at least one device to a neural network trained to output an estimated motion trajectory for performing the task. Further, the controller extracts at least some of the integer values of a solution to a mixed-integer optimization problem for planning an execution of the task that results in the estimated motion trajectory. Further, the controller solves the mixed-integer optimization problem for the parameters of the task with corresponding integer values fixed to the extracted integer values to produce an optimized motion trajectory subject to the constraint and changes the state of the at least one device to track the optimized motion trajectory.

Abstract: A system for generating a G-code for controlling an operation of a laser-cutting machine to cut parts from a sheet of material, upon receiving cutting data specifying a cutting order of parts and a cutting order of edges of each part, tests the parts for potential distortions and generates a G-code to avoid the potential distortion. For testing a current part, the system detects a potential distortion when the final edge of the current part is adjacent to an edge of a previously cut part scheduled for cutting before the current part according to the cutting order of parts. The system modifies the cutting order to select the modified cutting order for which the final edge is not adjacent to any edge of any previously cut part.