Abstract: Provided are methods for vehicle state estimation based on sensor data, which can include receiving the sensor data generated by one or more sensors, calculating a cornering stiffness value associated with the vehicle, predicting a lateral velocity value associated with the vehicle based on the cornering stiffness value, and outputting a set of vehicle state variables indicative of a current state of the vehicle at least by inputting the lateral velocity value into a recursive filter. Some methods described also include updating the cornering stiffness value based on the set of vehicle state variables, updating the lateral velocity value based on the updated cornering stiffness value, and updating the set of vehicle state variables based on the updated lateral velocity value. Systems and computer program products are also provided.

Abstract: Methods and systems are provided for using the weights of cost functions to improve linear-program-based vehicle driveline architectures and systems. In some embodiments, the methods and systems may include establishing values for driveline controls of a linear program based on driveline requests of the linear program. The values of the driveline controls, which may be used to adjust driveline actuators, may be established based on values of a plurality of weights of a cost function of the linear program, the weights respectively corresponding with the plurality of driveline requests.

Abstract: Methods and systems are provided for using the weights of cost functions to improve linear-program-based vehicle driveline architectures and systems. In some embodiments, the methods and systems may include establishing values for driveline controls of a linear program based on driveline requests of the linear program. The values of the driveline controls, which may be used to adjust driveline actuators, may be established based on values of a plurality of weights of a cost function of the linear program, the weights respectively corresponding with the plurality of driveline requests.

Abstract: The disclosed embodiments include a mass flow controller that implements a systematic sliding mode control algorithm that achieves robust and consistent flow-rate set-point tracking. Advantages of the disclosed embodiments include, but not limited to, rapid prototyping of easy-to-maintain embedded firmware and ease-of-tuning of controller parameters, which significantly reduces complexity of controller tuning. Other embodiments, advantages, and novel features are set forth in the detailed description.

Abstract: Robustly stabilizing controller bandwidth for a controlled mechanical system is determined as a function of the system's estimated inertia and dominant system parameters that define constraints on the bandwidth. In one or more embodiments, a bandwidth model is derived that defines a relationship between robustly stabilizing controller bandwidth and system gain over a range of reasonable system uncertainties. Using the model, a suitable controller bandwidth can be determined for a given motion control system given only estimates of the system gain and the dominant parameters(s) that constrain the bandwidth. In an example two-inertia system, the system gain and dominant parameter can comprise system inertia and mechanical coupling stiffness, respectively. Accordingly, estimates of the system inertia and coupling stiffness can be provided to the system, which determines a suitable controller bandwidth for the motion control system characterized by the estimates.

Abstract: Robustly stabilizing controller bandwidth for a controlled mechanical system is determined as a function of the system's estimated inertia and dominant system parameters that define constraints on the bandwidth. In one or more embodiments, a bandwidth model is derived that defines a relationship between robustly stabilizing controller bandwidth and system gain over a range of reasonable system uncertainties. Using the model, a suitable controller bandwidth can be determined for a given motion control system given only estimates of the system gain and the dominant parameters(s) that constrain the bandwidth. In an example two-inertia system, the system gain and dominant parameter can comprise system inertia and mechanical coupling stiffness, respectively. Accordingly, estimates of the system inertia and coupling stiffness can be provided to the system, which determines a suitable controller bandwidth for the motion control system characterized by the estimates.