Abstract: A method for drift detection and control for a vehicle may include determining an intentional drift probability of the vehicle based at least in part on one or more occupant inputs. The method further may include determining an initial aero bias upper bound using a first control system. The method further may include determining an initial aero bias command using a second control system. The method further may include determining a final aero bias upper bound based at least in part on the intentional drift probability, the initial aero bias upper bound, and the initial aero bias command. The method further may include controlling one or more aerodynamic actuators of the vehicle based at least in part on the final aero bias upper bound and the initial aero bias command.

Abstract: A method for active downforce control for a vehicle may include determining an out-of-phase interaction index between a driver of the vehicle and a controller. The controller is configured to control one or more aerodynamic actuators. The method further may include determining one or more active downforce control inputs based at least in part on the out-of-phase interaction index. The method further may include controlling the one or more aerodynamic actuators based at least in part on the one or more active downforce control inputs.

Abstract: A method for determining a desired lateral acceleration for a vehicle is provided. The method may include determining a first raw lateral acceleration request based at least in part on a steering wheel angle of the vehicle. The method further may include determining a maximum allowed lateral acceleration based at least in part on a tire slip model. The method further may include determining the desired lateral acceleration based at least in part on the first raw lateral acceleration request and the maximum allowed lateral acceleration. The method further may include adjusting an operation of the vehicle based at least in part on the desired lateral acceleration.

Abstract: A “configurator” sits logically between a control mechanism of a machine and an actuator of the machine. The configurator (which may, for example, be a separate hardware element or a method running on a computer processor) receives output signals from the control mechanism. Either those output signals contain a time-rate-of-change of the control mechanism or the configurator calculates the time-rate-of-change of the control mechanism by analyzing multiple control output signals. The configurator then alters a speed-of-response value of the actuator based on the control mechanism's time-rate-of-change and sends commands to the actuator based on its revised speed-of-response. The actuator responds to the commands to alter an aspect of the machine's behavior.

Abstract: A method for drift detection and control for a vehicle may include determining an intentional drift probability of the vehicle based at least in part on one or more occupant inputs. The method further may include determining an initial aero bias upper bound using a first control system. The method further may include determining an initial aero bias command using a second control system. The method further may include determining a final aero bias upper bound based at least in part on the intentional drift probability, the initial aero bias upper bound, and the initial aero bias command. The method further may include controlling one or more aerodynamic actuators of the vehicle based at least in part on the final aero bias upper bound and the initial aero bias command.

Abstract: A “configurator” sits logically between a control mechanism of a machine and an actuator of the machine. The configurator (which may, for example, be a separate hardware element or a method running on a computer processor) receives output signals from the control mechanism. Either those output signals contain a time-rate-of-change of the control mechanism or the configurator calculates the time-rate-of-change of the control mechanism by analyzing multiple control output signals. The configurator then alters a speed-of-response value of the actuator based on the control mechanism's time-rate-of-change and sends commands to the actuator based on its revised speed-of-response. The actuator responds to the commands to alter an aspect of the machine's behavior.

Abstract: A method for downforce control includes receiving sensor data from sensors, using a feedforward control to determine a first requested normal force at the first axle of the vehicle and a second requested normal force at the second axle of the vehicle and the sensor data, using a feedback control to determine a first requested normal force adjustment at the first axle of the vehicle and a second requested normal force adjustment at the second axle of the vehicle using the sensor data, fusing the first requested normal force at the first axle of the vehicle with the first requested normal force adjustment to determine a first-adjusted normal force request at the first axle, and fusing the second requested normal force with the second requested normal force adjustment to determine a second-adjusted normal force request at the second axle.

Abstract: A method for controlling a steer-by-wire system, comprising receiving vehicle data and a steering request from a vehicle, determining whether a steering road wheel actuator of the vehicle has failed using the vehicle data, in response to determining that the steering road wheel actuator of the vehicle has failed, determining a target wheel slip of the vehicle based on the steering request, maintaining the target wheel slip of the vehicle while the vehicle is in motion; and adjusting a wheel speed of at least one wheel of the vehicle based on a feedback signal, wherein the feedback signal is indicative of a road wheel angle while the vehicle is in motion.

Abstract: A method for downforce control includes receiving a plurality of vehicle inputs from a vehicle. The plurality of vehicle inputs includes sensor data from a plurality of sensors of the vehicle. The method further includes determining a downforce acting on the vehicle, by: (a) determining a predicted half-car model uncertainties using a neural network; and (b) determining a front normal force at the front axle and a rear normal force at the rear axle using the vehicle inputs, the predicted half-car model uncertainties, and a half-car model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the front normal force at the front axle and a rear normal force at the rear axle, respectively.

Abstract: Disclosed is an surface-coating robot operating system, a movable base (100) is provided to drive the whole operating system to implement autonomous navigation movement in the workspace; a vertical linear actuator (200) is used to drive a mechanical arm (300) to move up and down so as to meet the requirement for spraying at different heights; and the mechanical arm (300) is used to drive a spray gun (400) to implement multi-degree-of-freedom motion so as to meet the requirement for spraying at different positions; the whole spraying process is autonomously completed by the operating system, which is time-saving and labor-saving, high efficient, and ensures uniform spraying thickness, smooth spraying surface and consistent spraying quality; the robotic spraying may obviously reduce the coating dusts generated in a spraying process and the human risk caused by exposure to harmful coating chemicals. An operating method for surface-coating robot is disclosed.

Abstract: A method for controlling an autonomous vehicle includes receiving road data. The road data includes information about a plurality of potential events along the road ahead of the autonomous vehicle. The method further includes determining, in real time, a probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur while the autonomous vehicle moves along the road and determining, in real time, an adjusted planned path using a probabilistic predictive control that takes into account the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur. Further, the method includes controlling the autonomous vehicle to cause the autonomous vehicle to autonomously follow the adjusted planned path.

Abstract: A method for controlling a steer-by-wire system, comprising receiving vehicle data and a steering request from a vehicle, determining whether a steering road wheel actuator of the vehicle has failed using the vehicle data, in response to determining that the steering road wheel actuator of the vehicle has failed, determining a target wheel slip of the vehicle based on the steering request, maintaining the target wheel slip of the vehicle while the vehicle is in motion; and adjusting a wheel speed of at least one wheel of the vehicle based on a feedback signal, wherein the feedback signal is indicative of a road wheel angle while the vehicle is in motion.

Abstract: A fault remediation system for a vehicle includes one or more controllers in electronic communication with one or more consumed interfaces and one or more provided interfaces. The one or more controllers execute instructions to receive, from the one or more consumed interfaces, a consumed signal and perform fault detection upon the consumed signal to determine the presence of an active fault within the consumed signal. In response to detecting an active fault with the consumed signal, the one or more controllers select a remediation state from a group of two or more prospective remediation states based on a significance analysis of the consumed signal. The one or more controllers evaluate a relevant subfunction that corresponds to the consumed signal that the remediation state addresses for the presence of remediation tolerance and generates arbitration instructions based on the remediation tolerance.

Abstract: A method for downforce control includes receiving sensor data from sensors, using a feedforward control to determine a first requested normal force at the first axle of the vehicle and a second requested normal force at the second axle of the vehicle and the sensor data, using a feedback control to determine a first requested normal force adjustment at the first axle of the vehicle and a second requested normal force adjustment at the second axle of the vehicle using the sensor data, fusing the first requested normal force at the first axle of the vehicle with the first requested normal force adjustment to determine a first-adjusted normal force request at the first axle, and fusing the second requested normal force with the second requested normal force adjustment to determine a second-adjusted normal force request at the second axle.

Abstract: A method for downforce control includes receiving a plurality of vehicle inputs from a vehicle. The plurality of vehicle inputs includes sensor data from a plurality of sensors of the vehicle. The method further includes determining a downforce acting on the vehicle, by: (a) determining a predicted half-car model uncertainties using a neural network; and (b) determining a front normal force at the front axle and a rear normal force at the rear axle using the vehicle inputs, the predicted half-car model uncertainties, and a half-car model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the front normal force at the front axle and a rear normal force at the rear axle, respectively.

Abstract: A method for downforce control includes receiving vehicle inputs. The method includes determining a first normal-force request at the front axle and a second normal-force request at the rear axle using the purality of vehicle inputs and a prediction model. The prediction model is a combined state space model that integrates a half-car state space model and an actuator state space model, the half-car state space model is developed using a half-car model, and the actuator state space model is developed using a neural network model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the first normal-force request and the second normal-force request, respectively.

Abstract: A system for managing chassis and driveline actuators of a motor vehicle includes a control module executing program code portions that: cause sensors to obtain vehicle state information, receive a driver input and generate a desired dynamic output based on the driver input and the vehicle state information, and then estimate actuator actions based on the vehicle state information, generate one or more control action constraints based on the vehicle state information and estimated actuator actions, generate a reference control action based on the vehicle state information, the estimated actions of the one or more actuators and the control action constraints, and integrate the vehicle state information, the estimated actuator actions, desired dynamic output, reference control action and the control action constraints to generate an optimal control action that falls within a range of predefined actuator capacities and ensures driver control of the vehicle.

Abstract: A method for vehicle motion control includes receiving sensor data from a plurality of sensors of a vehicle and monitoring a vehicle response of the vehicle using the sensor data. The vehicle response is represented by a plurality of vehicle-response signals. The method further includes fusing the plurality of vehicle-response signals to obtain at least one fused signal. The method further includes determining whether to activate a vehicle stability control of the vehicle based on the at least one fused signal and commanding the vehicle to activate the vehicle stability control in response to determining to activate the vehicle stability control of the vehicle based on the at least one fused signal.

Abstract: A method for data driven downforce control of a vehicle includes receiving a first requested downforce at the front axle of a vehicle and a second requested downforce at the rear axle of the vehicle. The method further includes using a model-based control to determine a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the first requested downforce and the second requested downforce. The model-based control is based on a predetermined aerodynamic map. The method includes commanding the first aerodynamic actuator to move the first aerodynamic body to the first position. The method includes commanding the second aerodynamic actuator to move the second aerodynamic body to the second position.

Abstract: A method for controlling an autonomous vehicle includes receiving road data. The road data includes information about a plurality of potential events along the road ahead of the autonomous vehicle. The method further includes determining, in real time, a probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur while the autonomous vehicle moves along the road and determining, in real time, an adjusted planned path using a probabilistic predictive control that takes into account the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur. Further, the method includes controlling the autonomous vehicle to cause the autonomous vehicle to autonomously follow the adjusted planned path.