Abstract: Embodiments disclose a system for traffic control. The system comprises a receiver configured to receive traffic information indicative of a state of a traffic; a processor configured to determine one or more control commands to control at least a subset of vehicles forming the state of the traffic by minimizing a multiscale normalization of a difference between a flow of one or more vehicles of the subset of vehicles forming the traffic and a uniform flow of the one or more vehicles; and a transmitter configured to transmit the one or more control commands to the subset of vehicles.

Abstract: A control system for controlling an operation of a heating ventilation and air conditioning (HVAC) system is provided. The control system comprises an input interface configured to accept data indicative of a target distribution of thermal state in an environment, and a memory configured to store an airflow dynamics model (ADM) and an HVAC model. The control system further comprises a processor configured to inverse the ADM to estimate values of boundary conditions for inlet locations defining target thermal state at the inlet locations that result in the target distribution of thermal state in the environment; determine, using the HVAC model, target control parameters of actuators of the HVAC system resulting in the target thermal state at the inlet locations; and submit control commands to the HVAC system to operate the actuators of the HVAC system according to the control parameters.

Abstract: A control system for controlling an operation of a heating ventilation and air conditioning (HVAC) system is provided. The control system comprises an input interface configured to accept data indicative of a target distribution of thermal state in an environment, and a memory configured to store an airflow dynamics model (ADM) and an HVAC model. The control system further comprises a processor configured to inverse the ADM to estimate values of boundary conditions for inlet locations defining target thermal state at the inlet locations that result in the target distribution of thermal state in the environment; determine, using the HVAC model, target control parameters of actuators of the HVAC system resulting in the target thermal state at the inlet locations; and submit control commands to the HVAC system to operate the actuators of the HVAC system according to the control parameters.

Abstract: A control system for controlling a heating ventilation and air conditioning (HVAC) system, uses a thermal comfort model (TCM), an airflow dynamics model (ADM) and an HVAC model connecting thermal states at air vents with states of actuators of the HVAC system, for determining a target thermal state at the air vents connecting the HVAC system to an environment, such that the target thermal state at the air vents results in a thermal state at the location of an occupant according to the ADM connecting uneven distribution of thermal states at different locations in the environment. Further, the control system determines and submits control commands to one or multiple actuators of the HVAC system producing the target thermal state at the air vents.

Abstract: A traffic control system for controlling a traffic of cooperative and uncooperative vehicles determines delays on each segment of the roads of the network based on the current state of the flow of the traffic and determines flows of the cooperative and uncooperative vehicles that satisfy a Nash equilibrium for the cooperative vehicles having a common objective and the uncooperative vehicles having individual objectives. The system determines, for the cooperative vehicles, costs of segments of the roads that correspond to the Nash equilibrium and determines routes for the cooperative vehicles based on the current locations and target locations of the cooperative vehicles, and the costs of the segments of the roads in the network determined for the cooperative vehicles. The routes are transmitted to corresponding cooperative vehicles.

Abstract: A wind flow sensing system determines a first approximation of the velocity field at each of the altitudes by simulating computational fluid dynamics (CFD) of the wind flow with operating parameters reducing a cost function of a weighted combination of errors, determines a horizontal derivative of vertical velocity at each of the altitudes from the first approximation of the velocity fields, and determines a second approximation of the velocity fields using geometric relationships between a velocity field for each of the altitudes, projections of the measurements of radial velocities on the three-dimensional axes, and the horizontal derivative of vertical velocity for the corresponding velocity field. In the cost function of the CFD, each error corresponds to one of the altitudes and includes a difference between measured velocities at the line-of-site points at the corresponding altitude and simulated velocities at the line-of-site points simulated by the CFD for the corresponding altitude.

Abstract: A traffic control system for controlling a traffic of cooperative and uncooperative vehicles determines delays on each segment of the roads of the network based on the current state of the flow of the traffic and determines flows of the cooperative and uncooperative vehicles that satisfy a Nash equilibrium for the cooperative vehicles having a common objective and the uncooperative vehicles having individual objectives. The system determines, for the cooperative vehicles, costs of segments of the roads that correspond to the Nash equilibrium and determines routes for the cooperative vehicles based on the current locations and target locations of the cooperative vehicles, and the costs of the segments of the roads in the network determined for the cooperative vehicles. The routes are transmitted to corresponding cooperative vehicles.

Abstract: A system for optimizing a low-thrust trajectory of a spacecraft trajectory for orbital transfer includes an interface to receive data, a memory to store scheduled geostationary transfer orbit (GTO) data and scheduled geostationary Earth orbit (GEO) data and computer-executable programs, and a processor.

Abstract: A wind flow sensing system determines a first approximation of the velocity field at each of the altitudes by simulating computational fluid dynamics (CFD) of the wind flow with operating parameters reducing a cost function of a weighted combination of errors, determines a horizontal derivative of vertical velocity at each of the altitudes from the first approximation of the velocity fields, and determines a second approximation of the velocity fields using geometric relationships between a velocity field for each of the altitudes, projections of the measurements of radial velocities on the three-dimensional axes, and the horizontal derivative of vertical velocity for the corresponding velocity field. In the cost function of the CFD, each error corresponds to one of the altitudes and includes a difference between measured velocities at the line-of-site points at the corresponding altitude and simulated velocities at the line-of-site points simulated by the CFD for the corresponding altitude.

Abstract: Motion of multiple agents with identical non-linear dynamics is controlled to change density of the agents from the initial to the final density. A first control problem is formulated for optimizing a control cost of changing density of the agents from the initial density to the final density subject to dynamics of the agents in a density space. The first control problem, which is a non-linear non-convex problem over a multi-agent control and a density of the agents, is transformed into a second control problem over the density of the agents and a product of the multi-agent control and the density of the agents. The second control problem is a non-linear convex problem that is solved to produce the control input for each section of the state space. A motion of each agent is controlled according to a control input corresponding to its section of the state space.

Abstract: A method determines values of the airflow measured in the conditioned environment during the operation of the air-conditioning system and selects, from a set of regimes predetermined for the conditioned environment, a regime of the airflow matching the measured values of the airflow. The method selects, from a set of models of the airflow predetermined for the conditioned environment, a model of airflow corresponding to the selected regime and models the airflow using the selected model. The operation of the air-conditioning system is controlled using the modeled airflow.

Abstract: A method controls an operation of an air-conditioning system generating airflow in a conditioned environment. The method updates a model of airflow dynamics connecting values of flow and temperature of air conditioned during the operation of the air-conditioning system. The model is updated interactively iteratively to reduce an error between values of the airflow determined according to the model and values of the airflow measured during the operation. Next, the method models the airflow using the updated model and controls the operation of the air-conditioning system using the model.

Abstract: Motion of multiple agents with identical non-linear dynamics is controlled to change density of the agents from the initial to the final density. A first control problem is formulated for optimizing a control cost of changing density of the agents from the initial density to the final density subject to dynamics of the agents in a density space. The first control problem, which is a non-linear non-convex problem over a multi-agent control and a density of the agents, is transformed into a second control problem over the density of the agents and a product of the multi-agent control and the density of the agents. The second control problem is a non-linear convex problem that is solved to produce the control input for each section of the state space. A motion of each agent is controlled according to a control input corresponding to its section of the state space.

Abstract: A controller for controlling an operation of an air-conditioning system conditioning an indoor space includes a data input to receive state data of the space at multiple points in the space, a memory to store a code of a reinforcement learning algorithm and a history of the state data and a history of control commands having been applied to the air-conditioning system, wherein the history of the control commands is associated with the state data and history of rewards, a processor coupled to the memory determines a value function outputting a cumulative value of the rewards and transmits a control command by using the reinforcement learning algorithm, and a data output to receive the control command from the processor and transmit a control signal to the air-conditioning system, wherein the control signal controls at least one actuator of the air-conditioning system according to the control command.

Abstract: A method controls an operation of an air-conditioning system generating airflow in a conditioned environment. The method updates a model of airflow dynamics connecting values of flow and temperature of air conditioned during the operation of the air-conditioning system. The model is updated interactively iteratively to reduce an error between values of the airflow determined according to the model and values of the airflow measured during the operation. Next, the method models the airflow using the updated model and controls the operation of the air-conditioning system using the model.

Abstract: A method determines values of the airflow measured in the conditioned environment during the operation of the air-conditioning system and selects, from a set of regimes predetermined for the conditioned environment, a regime of the airflow matching the measured values of the airflow. The method selects, from a set of models of the airflow predetermined for the conditioned environment, a model of airflow corresponding to the selected regime and models the airflow using the selected model. The operation of the air-conditioning system is controlled using the modeled airflow.

Abstract: A method estimates a state of a spacecraft in a planet-moon environment by executing iteratively a particle filter. The particle filter comprising integrates individually states of each particle of the particle filter according to a probability-evolution equation using a model of the state of the spacecraft represented as a planar circular restricted three-body problem and determines a prior probability of each particle as a previous posterior probability of a corresponding particle during a previous iteration. A joint probability distribution of the state of the spacecraft is determines using the states of each particle and the prior probabilities of each particle and the states and the prior probabilities of each particle are updated according to the joint probability distribution of the state of the spacecraft and a measurement of the state of the spacecraft to produce posterior probabilities of each particle.

Abstract: A motion of an object is controlled from a geostationary transit orbit (GTO) of an earth to an orbit of a moon. A first trajectory of the motion of the object is determined from an intermediate orbit of an earth to a neighborhood of a stable manifold of a first Lagrange point (L1). A second trajectory of the motion of the object is determined from the GTO to the intermediate orbit. A third trajectory of the motion of the object is determined from the neighborhood to the stable manifold to an L1 orbit, and a fourth trajectory of the motion of the object is determined from the L1 orbit to the orbit of the moon. A trajectory from the GTO to the orbit of the moon is determined based on a combination of the first, the second, the third, and the fourth trajectories.

Abstract: A method estimates a state of a spacecraft in a planet-moon environment by executing iteratively a particle filter. The particle filter comprising integrates individually states of each particle of the particle filter according to a probability-evolution equation using a model of the state of the spacecraft represented as a planar circular restricted three-body problem and determines a prior probability of each particle as a previous posterior probability of a corresponding particle during a previous iteration. A joint probability distribution of the state of the spacecraft is determines using the states of each particle and the prior probabilities of each particle and the states and the prior probabilities of each particle are updated according to the joint probability distribution of the state of the spacecraft and a measurement of the state of the spacecraft to produce posterior probabilities of each particle.

Abstract: A motion of an object is controlled from a geostationary transit orbit (GTO) of an earth to an orbit of a moon. A first trajectory of the motion of the object is determined from an intermediate orbit of an earth to a neighborhood of a stable manifold of a first Lagrange point (L1). A second trajectory of the motion of the object is determined from the GTO to the intermediate orbit. A third trajectory of the motion of the object is determined from the neighborhood to the stable manifold to an L1 orbit, and a fourth trajectory of the motion of the object is determined from the L1 orbit to the orbit of the moon. A trajectory from the GTO to the orbit of the moon is determined based on a combination of the first, the second, the third, and the fourth trajectories.