Abstract: A system jointly controls vehicles according to traffic configuration of a zone of intersection of a first road and a second road. The intersection zone includes a sequencing zone and a control zone covering sections of the first and the second road in proximity to the intersection. The system groups vehicles traveling within the sequencing zone on the first and the second roads into a set of groups of the vehicles and prevents the vehicles of different groups to travel concurrently in the control zone such that the first vehicle of the following group cannot pass the last vehicle of the preceding group. The system determines motion trajectories for the vehicles of the same group traveling in the control zone on the first and the second roads to pass the intersection and transmits the motion trajectories to the corresponding vehicles.

Abstract: A control system for controlling a motion of a vehicle to a target driving goal uses a decision-maker configured to determine a sequence of intermediate goals leading to the next target goal by optimizing the motion of the vehicle subject to a first model and tightened driving constraints formed by tightening driving constraints by a safety margin, and uses a motion planner configured to determine a motion trajectory of the vehicle tracking the sequence of intermediate goals by optimizing the motion of the vehicle subject to the second model. The driving constraints include mixed logical inequalities of temporal logic formulae specified by traffic rules to define an area where the temporal logic formulae are satisfied, while the tightened driving constraints shrink the area by the safety margin, which is a function of a difference between the second model and the first model approximating the second model.

Abstract: A control system for controlling an operation of a machine subject to constraints including equality and inequality constraints on state and control variables of the system iteratively solves an optimal control structured optimization problem (OCP), such that each iteration outputs primal variables and dual variables with respect to the equality constraints and dual variables and slack variables with respect to the inequality constraints. For a current iteration, the system classifies each of the inequality constraints as an active, an inactive or an undecided constraint based on a ratio of a slack variable to a dual variable of the corresponding inequality constraint determined by a previous iteration, finds an approximate solution to the set of relaxed optimality conditions subject to the equality constraints and the active and undecided inequality constraints, and update the primal, dual, and slack variables for each of the equality and inequality constraint.

Abstract: A control system of a vehicle for controlling motion of the vehicle traveling on a road shared with a set of moving objects include a memory to store a set of regions of states of lateral dynamic of the vehicle corresponding to a set of equilibrium points. Each region defines a control invariant set of the states of the lateral dynamic determined for different speeds of the vehicle, such that the vehicle having a state within a region determined for a speed is capable to maintain its states within the region while moving with the speed. Each region includes a corresponding equilibrium point and intersects with at least one adjacent region. Each equilibrium point is associated with one or multiple regions determined for different speeds.

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: A control system controls a vehicle using a probabilistic motion planner and an adaptive predictive controller. The probabilistic motion planner produces a sequence of parametric probability distributions over a sequence of target states for the vehicle with parameters defining a first and higher order moments. The adaptive predictive controller optimizes a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle. The cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments. The balancing is performed by weighting different state variables using one or multiple of the higher order moments of the probability distribution.

Abstract: A control system for wheel-slip prevention in a railway vehicle with a pneumatic brake is provided. The control system comprises an input interface configured to accept a deceleration reference for controlling the pneumatic brake, and a memory configured to store a reference governor providing executable instructions for modifying the deceleration reference upon its violation of a wheel-slip constraint, and configured to store a controller providing executable instructions for mapping the modified deceleration reference to a sequence of control commands for controlling pressure applied by the pneumatic brake. The control system further comprises a processor configured to execute the reference governor to modify the deceleration reference and configured to execute the controller to map the modified deceleration reference to the sequence of control commands. Further, an output interface of the control system is configured to output the sequence of control commands to control the pneumatic brake.

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 controller for controlling a system with continuous and discrete elements of operation accepts measurements of a current state of the system, solves a mixed-integer model predictive control (MI-MPC) problem subject to state constraints on the state of the system to produce control inputs to the system, and submits the control inputs to the system thereby changing the state of the system. To solve the MI-MPC, the controller transforms the state constraints into state-invariant control constraints on the control inputs to the system, such that any combination of values for the control inputs, resulting in a sequence of values for the state variables that satisfy the state constraints, also satisfy the state-invariant control constraints, and solve the MI-MPC problem subject to the state constraints and the state-invariant control constraints.

Abstract: A control system uses a set of regions of states of lateral dynamic of the vehicle corresponding to a set of equilibrium points to control a vehicle. Each region defines a control invariant set of the states of the lateral dynamic determined such that the vehicle having a state within a region is capable to maintain its states within the region. Each region includes a corresponding equilibrium point and intersects with at least one adjacent region. The control system identifies collision-free regions at different time steps of control to produce a collision free sequence of regions forming a union of regions in space and time connecting a region including an initial displacement with a region including a target displacement. The control system produces a trajectory within the union connecting the initial displacement with the target displacement and control the vehicle according to the trajectory.

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 positioning system for a global navigational satellite system (GNSS) includes a receiver to receive carrier signals and code signals transmitted from a set of GNSS satellites that include a carrier phase ambiguity as an unknown integer number of wavelengths of the carrier signal traveled between the satellite and the receiver, and a processor to track a position of the receiver. The processor is configured to determine a set of possible combinations of integer values of the carrier phase ambiguities consistent with the measurements of the carrier signal and the code signal according to one or combination of the motion model and the measurement model within bounds defined by one or combination of the process noise and the measurement noise and execute a set of position estimators determining positions of the receiver using different combinations of integer values of the carrier phase ambiguities selected from the set of possible combinations.

Abstract: A spacecraft including a set of thrusters for changing a pose of the spacecraft. At least two thrusters mounted on a gimbaled boom assembly and are coupled together sharing the same gimbal angle. A model predictive controller (MPC) to produce a solution for controlling thrusters of the spacecraft by optimizing a cost function over a receding horizon using a model of dynamics of the spacecraft effecting a pose of the spacecraft and a model of dynamics of momentum exchange devices of the spacecraft effecting an orientation of the spacecraft. A modulator to modulate magnitudes of the thrust of the coupled thrusters determined by the MPC as pulse signals specifying ON and OFF states of each of the coupled thruster, wherein the ON states of the coupled thrusters sharing the same gimbal angle do not intersect in time. A thruster controller to operate the thrusters according to their corresponding pulse signals.

Abstract: A spacecraft including a spacecraft bus and a set of thrusters for changing a pose of the spacecraft. Wherein at least two thrusters are mounted on a gimbaled boom assembly connecting the two thrusters with the spacecraft bus, such that the two thrusters are coupled thrusters sharing the same gimbal angle. A model predictive controller to produce a solution for controlling thrusters of the spacecraft by optimizing a cost function over multiple receding horizons. The cost function is composed of a cost accumulated over the multiple receding horizons, including a cost accumulated over a first horizon using a dynamics governing a north-south position of the spacecraft, and a cost accumulated over a second horizon using a model of dynamics of the spacecraft governing an east-west position. A thruster controller to operate the thrusters according to their corresponding signals.

Abstract: A multi-satellite system includes a first satellite and a second satellite configured to be separated in a predetermined distance between the satellites after being launched into space, and a tether including an optical fiber having first and second ends, wherein the first end is connected to the first satellite and the second end is connected to the second satellite, wherein a length of the tether is greater than the predetermined distance. In this case, the first satellite includes an optical transceiver connected to the first end of the optical fiber to provide a communications link to the second satellite, a spool containing partial winding of the tether, and a free space optical transceiver to provide a first communications link to a first distant satellite.

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: A control system for controlling an operation of a spacecraft. A model predictive controller (MPC) produces a solution for controlling thrusters of the spacecraft. The MPC optimizes a cost function over a finite receding horizon using a model of dynamics of the spacecraft effecting a pose of the spacecraft and a model of dynamics of momentum exchange devices of the spacecraft effecting an orientation of the spacecraft. The optimization is subject to hard and soft constraints on angles of thrusts generated by thrusters. Further, the hard constraints require the angles of thrusts in the solution to fall within a predetermined range defined by the hard constraints. The soft constraints penalize the solution for deviation of the angles of thrusts from nominal angles corresponding to a torque-free thrust passing through the center of the mass of the spacecraft. A thruster controller operates the thrusters according to the solution of the MPC.

Abstract: A predictive controller controls a vehicle subject to equality and inequality constraints on state and control variables of the vehicle and solves a matrix equation of necessary optimality conditions to produce control inputs to control the vehicle. The controller represents the state variables as a function of the control variables using discrete-time dynamics and determines the control inputs iteratively using two levels of iterations. The first level of iterations selects active inequality constraints, updates a constraint Jacobian matrix with a low-rank update for a change in the set of active inequality constraints to include the active inequality constraints, and updates a preconditioning matrix with a low-rank factorization update, in response to the low-rank update of the constraint Jacobian matrix. The second level of iterations, nested in the first level, solves the matrix equation with the updated constraint Jacobian matrix using the updated preconditioning matrix to produce the control inputs.

Abstract: Controlling a vehicle by producing a sequence of intermediate goals that includes specific goals, additional specific goals and optional goals, from a travel route for a prediction horizon. Testing a feasibility of each intermediate goal (IG), using a first model of motion of the vehicle and a first model of motion of the traffic, by the IG being achieved by satisfying traffic conditions and motion capabilities of the vehicle, after achieving the IG, a next goal of the specific goals and the additional specific goals can also be achieved. Compute a trajectory for each feasible IG, using a second model of motion of the vehicle and a second model of motion of the traffic, and compare each computed trajectory according to a numerical value determined by a cost function and determine satisfaction of constraints on the movement of the vehicle and the vehicle interaction with the road and the traffic.

Abstract: A control system for controlling an operation of a processing machine positioning a worktool according to a processing pattern to machine a workpiece. A memory to store a reference trajectory defined in a spatial domain by a sequence of points for positioning the worktool and defined in a time domain by a relative time for positioning the worktool on each point of the reference trajectory. A sensor to determine a state of the processing machine. A reference governor to iteratively process the reference trajectory over a receding horizon including multiple windows of points, and analytically update the relative time for positioning the worktool for some points of the reference trajectory within the receding horizon to satisfy constraints on the operation of the processing machine having the state. A controller to control the operation of the processing machine using control inputs causing the worktool to track the updated reference trajectory.