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 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: An extremum seeking control system and a method for controlling a system is provided. The method comprises receiving a tracking error, consecutive measurements of a system output, and consecutive measurements of a cost function of the system, and estimating a gradient based on the tracking error, the consecutive measurements of the system output, and the consecutive measurements of the cost function of the system. The method further comprises determining a step-size based on a ratio of a covariance of the gradient to the gradient, and determining a set-point optimizing performance of the system, based on the gradient and the step-size. The method further comprises controlling the system based on the set-point optimizing performance of the system.

Abstract: The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus (T1DM). The energy consumption of the AP can be lowered by reducing updates of the control model: the number of times the decisionmaking algorithm is invoked to compute an appropriate insulin dose.

Abstract: An apparatus for controlling an operation of a system is provided. The apparatus comprises an input interface configured to receive a state trajectory of the system, and a memory configured to store a model of dynamics of the system including a combination of at least one differential equation and a closure model. The apparatus further comprises a processor configured to update the closure model using reinforcement learning (RL) having a value function reducing a difference between a shape of the received state trajectory and a shape of state trajectory estimated using the model with the updated closure model, and determine a control command based on the model with the updated closure model. Further, the apparatus comprises an output interface configured to transmit the control command to an actuator of the system to control the operation of the 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 a method for controlling an operation of HVAC system is provided. The system comprises at least one processor; and memory having instructions stored thereon that, when executed by the at least one processor, cause the system to receive data indicative of the operation of the HVAC system and optimize a cost function to reduce a difference between a full thermal state of the conditioned environment simulated from the received data based on a physical model of airflow including a partial differential equation (PDE) and a thermal state of the conditioned environment reconstructed by an observer using a reduced order model (ROM) of airflow including an ordinary differential equation (ODE) from values of the full thermal state at a set of locations, wherein optimization of the cost function is a multivariable optimization of a structure of the observer and a structure of the ODE.

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 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 vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle.

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

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: 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 system and method for detecting anomalous sound are disclosed. The method includes receiving a spectrogram of an audio signal with elements defined by values in a time-frequency domain of the spectrogram. Each of the values corresponds to an element of the spectrogram that is identified by a coordinate in the time-frequency domain. The time-frequency domain of the spectrogram is partitioned into a context region and a target region. The context region and the target region are processed by a neural network using an attentive neural process to recover values of the spectrogram for elements with coordinates in the target region. The recovered values of the elements of the target region are compared with values of elements of the partitioned target region. An anomaly score is determined based on the comparison. The anomaly score is used for performing a control action.

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 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 an autonomous vehicle includes a memory configured to store parameters of a g-g plot defining admissible space of values of longitudinal and lateral accelerations. The g-g plot parameters define a mapping between user driving preferences and constrained control of the autonomous vehicle. The g-g plot parameters include a maximum forward acceleration, a maximum backward acceleration, a maximum lateral acceleration and a shape parameter defining profile of curves connecting maximum values of forward, backward, and lateral accelerations. The system accepts a comfort level as a feedback from a passenger of the vehicle, determines a dominant parameter corresponding to the feedback, updates the dominant parameter of the g-g plot based on the comfort level indicated in the feedback, and controls the vehicle to maintain dynamics of the vehicle within the admissible space defined by the parameters of the updated g-g plot.

Abstract: A system and a method for calibrating a model of thermal dynamics of thermal state in an environment of a building conditioned by an operation of a heating, ventilating, and air-conditioning (HVAC) system is provided. The method includes receiving values of the control inputs to the actuators of the HVAC system and values of the thermal state at locations of the environment caused by the operation of the HVAC system according to the values of the control inputs, and computing a probabilistic surrogate model iteratively, using a Bayesian optimization, until a termination condition is met. The method further comprises outputting, when the termination condition is met, an optimal combination of the different parameters of the model of thermal dynamics having the largest likelihood of being a global minimum at the probabilistic surrogate model according to an acquisition function of the first two order moments of the calibration errors.

Abstract: A control system for controlling a set of actuators of a system. The control system comprising a switcher configured to select a subset of setpoints from a set of setpoints that control the corresponding set of actuators. An extremum-seeking controller (ESC) configured to perturb a subset of setpoints at each iteration based on a probabilistic distribution of partial gradients of a cost function relating values of the subset of setpoints to a cost of operation of the system. A stochastic gradient estimator is configured to estimate a full gradient of the cost function and update the estimation of the full gradient based on the probabilistic distribution of the partial gradients generated at each ESC iteration. A feedback controller is configured to drive a state of the subset of actuators of the system towards the corresponding perturbed subset of setpoints.