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 distributed machine learning based traffic prediction method is provided for predicting traffic of roads. In this case, the distributed machine learning based traffic prediction method includes distributing global multi-task traffic models by a learning server to learning agents to locally train the traffic models, uploading locally trained traffic models by learning agents to the learning server, updating global multi-task traffic models by the learning server using locally trained traffic model parameters acquired from learning agents, generating a time-dependent global traffic map by the learning server using the well trained global multi-task traffic models, distributing the time-dependent global traffic map to vehicles traveling on the roads, and computing an optimal travel route with the least travel time by a vehicle using the time-dependent global traffic map based on a driving plan.

Abstract: An apparatus for controlling a system includes a memory to store a model of the system including a motion model of the system subject to process noise and a measurement model of the system subject to measurement noise, such that one or combination of the process noise and the measurement noise forms an uncertainty of the model of the system with unknown probabilistic parameters, wherein the uncertainty of the model of the system causes a state uncertainty of the system with unknown probabilistic parameters. The apparatus also includes a sensor to measure a signal to produce a sequence of measurements indicative of a state of the system, a processor to estimate a Gaussian distribution representing the state uncertainty, and a controller to determine a control input to the system using the model of the system with state uncertainty represented by the Gaussian distribution and control the system according to the control input.

Abstract: A probabilistic system for tracking a state of a vehicle using unsynchronized cooperation of information includes a probabilistic multi-head measurement model relating incoming measurements with the state of the vehicle. The first head of the model relates measurements of the satellite signals subject to measurement noise with a belief on the state of the vehicle, and a second head relates an estimation of the state of the vehicle subject to estimation noise with the belief on the state of the vehicle. A probabilistic filter of the system updates recursively the belief on the state of the vehicle based on the multi-head measurement model accepting one or a combination of the measurements of the satellite signals subject to the measurement noise and the estimation of the state of the vehicle subject to the estimation noise.

Abstract: A server jointly tracks states of multiple vehicles using measurements of satellite signals received at each vehicle and parameters of the probabilistic distribution of the state of each vehicle. The server fuse states and measurements into an augmented state of the multiple vehicles and an augmented measurement of the augmented state subject to augmented measurement noise defined by a non-diagonal covariance matrix with non-zero off-diagonal elements, each non-zero off-diagonal elements relating errors in the measurements of a pair of corresponding vehicles. The server executes a probabilistic filter updating the augmented state and fuses the state of at least some of the multiple vehicles with a corresponding portion of the updated augmented state.

Abstract: A controller of a machine determines jointly a sequence of control inputs defining a state trajectory of the machine and a desired knowledge of the environment by solving a multivariable constrained optimization of a model of dynamics of the machine relating the state trajectory with the sequence of control inputs subject to a constraint on admissible values of the states and the control inputs defined based on the desired knowledge of the surrounding environment represented by the state of the environment and the uncertainty of the state of the environment determined from the measurements of the environment. In such a manner, the controller performs joint but imbalance optimization of the control inputs and the sensing instructions to the sensor for learning the environment.

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 system is controlled by solving a mixed-integer optimal control optimization problem using branch-and-bound (B&B) optimization that searches for a global optimal solution within a search space. The B&B optimization iteratively partitions the search space into a nested tree of regions, and prunes at least one region from the nested tree of regions before finding a local optimal solution for each region when a dual objective value of a projection of a sub-optimal dual solution estimate for each region into a dual feasible space is greater than an upper bound or lesser than a lower bound of the global optimal solution maintained by the B&B optimization.

Abstract: A predictive controller controls a system under uncertainty subject to constraints on state and control variables of the system. At each control step, the predictive controller solves an inequality constrained nonlinear dynamic optimization problem including probabilistic chance constraints representing the uncertainty to produce a control command, and controls an operation of the system using the control command. The predictive controller solves the dynamic optimization problem based on a two-level optimization that alternates, until a termination condition is met, propagation of covariance matrices of the probabilistic chance constraints within the prediction horizon for fixed values of the state and control variables with optimization of the state and control variables within the prediction horizon for fixed values of the covariance matrices.

Abstract: A traffic control system for controlling traffic at interconnected intersections is provided, where the system comprises a receiver that receives traffic data that indicates states of vehicles approaching an intersection of the interconnected intersections and directions of the vehicles exiting the intersection. Further, the system comprises a processor that determines intersection crossing times and velocities of vehicles approaching the intersection by minimizing at least one of a total travel time or a maximum travel time of the vehicles for crossing the intersection. The contribution of each vehicle of the vehicles approaching the intersection in the at least one of a total travel time or a maximum travel time is weighted based on directions of the vehicles and traffic at next intersection. Further, the system comprises a transmitter that transmits the intersection crossing times and velocities to the vehicles exiting the intersection for controlling the traffic at the interconnected intersections.

Abstract: Controller of a vehicle uses control functions to transition the current state of the vehicle into a target state. A control function is probabilistic to output a parametric probability distribution over the target state defined by a first moment and at least one higher order moment. The controller submits the current state into at least a subset of control functions consistent with the next driving decision to produce a subset of parametric probability distributions over the target state, combines the subset of parametric probability distributions to produce a joint parametric probability distribution of the target state, and determines the control command based on the first moment and at least one higher order moment of the joint parametric probability distribution of the target state.

Abstract: A system is controlled by solving a mixed-integer optimal control optimization problem using branch-and-bound (B&B) optimization that searches for a global optimal solution within a search space. The B&B optimization iteratively partitions the search space into a nested tree of regions, and prunes at least one region from the nested tree of regions before finding a local optimal solution for each region when a dual objective value of a projection of a sub-optimal dual solution estimate for each region into a dual feasible space is greater than an upper bound or lesser than a lower bound of the global optimal solution maintained by the B&B optimization.

Abstract: A controller controls a spacecraft to rendezvous a non-center-of-mass point of the controlled spacecraft with a non-center-of-mass point of an uncontrolled celestial body. The controlled spacecraft and the uncontrolled celestial body form a multi-object celestial system, and the controller produces control commands to thrusters of the controlled spacecraft using a non-linear model predictive control (NMPC) optimizing a cost function over a receding horizon that minimizes an error between coordinates of the non-center-of-mass point of the spacecraft and the non-center-of-mass point of the celestial body subject to joint dynamics of the multi-object celestial system coupled with joint kinematics of the multi-object celestial system.

Abstract: Drift-based rendezvous control system for controlling an operation of a spacecraft to rendezvous the spacecraft to a goal region over a finite time (FT) horizon. The system including accepting data including values of spacecraft states at a specified time period within the FT horizon. A processor at the specified time period selects a set of drift regions corresponding to a desired goal region at a location on an orbit where the target is located at the specified time period. Update a controller having a model of dynamics of the spacecraft with the accepted data. Formulate the set of drift regions as a penalty in a cost function of the updated controller. Generate control commands resulting in a real-time drift-based control policy where upon entering the drift region, the thrusters are turned off in order to minimize an amount of operation of the thrusters while rendezvousing with the desired goal region.

Abstract: A system control a vehicle using a friction function describing a friction between a type of surface of the road and a tire of the vehicle as a function of a slip of a wheel of the vehicle. The parameters of each friction function include an initial slope of the friction function defining a stiffness of the tire and one or combination of a peak friction, a shape factor and a curvature factor of the friction function. Upon estimating a slip and a stiffness of the tire, the system selects from the memory parameters of the friction function corresponding to the current stiffness of the tire, determines a control command using a value of the friction corresponding to the slip of the tire according to the friction function defined by the selected parameters, and submits the control command to an actuator of the vehicle.

Abstract: Systems and methods controlling an operation of a vehicle in real time to rendezvous the vehicle with a target over a finite time horizon having multiple specified time periods. Select a set of unsafe regions from stored unsafe regions, the set of unsafe regions represents regions of space around the target in which any operation of the PSNO thrusters does not avoid collision with the target, guaranteeing collision trajectories with the target. Formulating the set of unsafe regions as safety constraints, and updating a controller having a model of dynamics of the vehicle with the accepted data. Generating control commands by subjecting the updated controller to the safety constraints to produce a rendezvous trajectory that avoids the set of unsafe regions, guaranteeing an operation of at least the PSNO thrusters, in the event of partial vehicle thruster failure results in a trajectory that does not collide with the target.

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: A system for controlling an operation of a vehicle to rendezvous with a target over a finite time horizon, wherein the vehicle and the target form a multi-object celestial system. A processor to formulate passive unsafe regions as passive safety constraints. The passive unsafe regions represents regions of space around the target guaranteeing collision trajectories with the target, in an event of total thruster failure. Update a controller having a model of dynamics of the vehicle with received data, and subject the updated controller to the passive safety constraints to generate control commands that produce a collision free rendezvous trajectory which avoids unsafe regions for the specified time period, guaranteeing a collision free trajectory with respect to the target in the event of the total vehicle thruster failure, so the vehicle does not collide with the target. Output the control commands to activate or not activate thrusters of the vehicle.

Abstract: A predictive controller controls a system under uncertainty subject to constraints on state and control variables of the system. At each control step, the predictive controller solves an inequality constrained nonlinear dynamic optimization problem including probabilistic chance constraints representing the uncertainty to produce a control command, and controls an operation of the system using the control command. The predictive controller solves the dynamic optimization problem based on a two-level optimization that alternates, until a termination condition is met, propagation of covariance matrices of the probabilistic chance constraints within the prediction horizon for fixed values of the state and control variables with optimization of the state and control variables within the prediction horizon for fixed values of the covariance matrices.

Abstract: Methods and systems including processor configured to receive a transition indication to switch a semi-autonomous vehicle from a driving mode to another driving mode while a driver is driving the vehicle on a roadway. Wherein the driving modes include an autonomous lane keeping mode, an autonomous lane change mode and a manual mode. In response to receiving the request, use protocol data to assess a status of the vehicle's environment, a vehicle state and systems of the vehicle. Select, a set of conditions corresponding to the vehicle's state, based on one or more of the assessments. Determine, whether a switch between driving modes is initiated by the driver, by verifying if the vehicle's state satisfies the set of conditions, in order to validate if the driver actuated the vehicle into or out of a safety region.