System for controlling at least one active magnetic bearing equipping a rotating machine comprising a rotor and a stator, at least one means for measuring the radial positions of the rotor as a function of the signal from at least one position sensor, and at least two control loops of the active magnetic bearing as a function of the radial positions of the rotor, each control loop of the magnetic bearing being provided with at least one synchronous filter as a function of the rotation speed, and an extended Kalman filter for determining the rotation speed of the rotor with respect to the stator receiving as input, from position sensors, measurements of radial position of the rotor and as a function of measurements of radial position of the rotor performed over a predetermined time at zero rotor rotation speed.

Disclosed are a vehicle navigation guidance system and a vehicle. The system includes: a navigation controller, a steering angle sensor, a motor steering controller and a display controller. The steering angle sensor is communicatively connected to the navigation controller, and is configured to acquire rotational angular velocity information of a wheel relative to a vehicle body, and output the angular velocity information to the navigation controller. The navigation controller is configured to output navigation guidance information according to positioning information and the angular velocity information, where the navigation controller includes a first positioning device, and the first positioning device is configured to acquire the positioning information. The motor steering controller is communicatively connected to the navigation controller, and is configured to perform steering control according to the navigation guidance information. The display controller is communicatively connected to the navigation controller, and is configured to display the navigation guidance information.

The disclosure provides a method for controlling a drive arrangement for a flap of a vehicle by a control arrangement, wherein in a measuring routine a measured movement value is measured by a movement measuring system. It is proposed that in an estimation routine an estimated movement value is estimated, in a movement routine the estimated movement value is determined via the estimation routine and the flap is adjusted according to a set movement value, in a correction routine the estimated movement value is determined from the estimation routine and an estimation error is determined and from the estimated movement value and according to a correction specification the set of model parameters is corrected based on the estimation error and supplied to the estimation routine and in the correction routine the correction of the set of model parameters is performed for a part section of the motorized movement cycle.

A method for estimating the position of a rotor of a synchronous electrical machine, includes a rotor and a stator coupled to an inverted synchronous electrical machine via a rectifier comprising the following steps: measurement of a current i.sub.abc circulating in the stator of the synchronous electrical machine; determination of two signals in quadrature i.sub.α; i.sub.β according to a stationary reference frame from the current i.sub.abc and isolation of two filtered signals i.sub.αh; i.sub.βh from the two signals in quadrature i.sub.α; i.sub.β; demodulation of the two filtered signals i.sub.αh; i.sub.βh in order to obtain two demodulated signals i.sub.αobs, i.sub.βobs, obtaining of an estimated position {circumflex over (θ)} of the rotor from the two demodulated signals i.sub.αobs, i.sub.βobs.

A method determines the total mass of an automotive vehicle on the basis of data of a communication network and parameters of the vehicle, in which an estimation of the total laden mass (mv,est) of the vehicle, of the speed of the vehicle (vest) and of the slope of the road (αest) is determined at an instant (k) by applying the fundamental equation of dynamics and as a function of the values of the total mass of the vehicle, of the speed of the vehicle and of the slope of the road at a previous instant (k−1).

A method for estimating the position of a rotor of a synchronous electrical machine, includes a rotor and a stator coupled to an inverted synchronous electrical machine via a rectifier comprising the following steps: measurement of a current i.sub.abc circulating in the stator of the synchronous electrical machine; determination of two signals in quadrature i.sub.α; i.sub.β according to a stationary reference frame from the current i.sub.abc and isolation of two filtered signals i.sub.αh; i.sub.βh from the two signals in quadrature i.sub.α; i.sub.β; demodulation of the two filtered signals i.sub.αh; i.sub.βh in order to obtain two demodulated signals i.sub.αobs, i.sub.βobs; obtaining of an estimated position {circumflex over (θ)} of the rotor from the two demodulated signals i.sub.αobs, i.sub.βobs.

A method for evaluating sensor data. In the method, firstly, raw sensor data and/or processed sensor data from at least one sensor are input and measurement data determined from the raw sensor data and/or the processed sensor data. The measurement data are then corrected on the basis of a mathematical model, wherein, on correction, drift of the raw sensor data and/or of the processed sensor data is determined and removed from the measurement data. The corrected measurement data are furthermore output.

Embodiments described herein include processes for generating a hybrid model for modeling processes in semiconductor processing equipment. In a particular embodiment, method of creating a hybrid machine learning model comprises identifying a first set of cases spanning a first range of process and/or hardware parameters, and running experiments in a lab for the first set of cases. The method may further comprise compiling experimental outputs from the experiments, and running physics based simulations for the first set of cases. In an embodiment, the method may further comprise compiling model outputs from the simulations, and correlating the model outputs with the experimental outputs with a machine learning algorithm to provide the hybrid machine learning model.

Methods and apparatuses for determining operational parameters of a blood pump comprising a rotor which transports the blood are provided. The change in the behaviour of at least one first and one second operational parameter, independently from each other, of the pump, is determined. A determination of the flow through the pump and/or the difference in pressure across the pump and/or the viscosity of the blood takes into account the determined change in behaviour of the at least two operational parameters. A modelling for a dynamic model of the known quantities may be carried out and an estimation method using a Kalman filter may be used.

Device (1) and method for detecting battery cell states, BZZ, and/or battery cell parameters, BZP, of at least one battery cell (BZ), comprising a dual Kalman filter (2) which includes a state estimator (2A) for estimating battery cell states, BZZ, and a parameter estimator (2B) for estimating battery cell parameters, BZP, and comprising a determination unit (3) which is suitable for determining noise components (n, v) of the state estimator (2A) and of the parameter estimator (2B) on the basis of a stored characteristic parameter behaviour of the battery cell (BZ), wherein the battery cell states, BZZ, and the battery cell parameters, BZP, can be adapted automatically to a specified battery model (BM) of the battery cell (BZ) by means of the dual Kalman filter (2) on the basis of the noise components (n, v) determined by the determination unit.