Speed sensorless vector control of induction motors with parameter estimation

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method will evaluate rotor velocity, /spl omega//sub r/, on-line, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRAS or Kalman filters. Furthermore, the new scheme will operate at all velocities including zero with very little error. The procedure employs motor model equations, however all differential and integral terms have been eliminated giving a very fast, low-cost, effective and practical alternative to the current available methods. Simulation results verify the operation of the scheme under ideal and PWM conditions.

Angular Velocity(AV) estimation is necessary for controllers used for rotation motion monitoring. The velocity estimated is used for feedback and is very crucial in speed control for a variety of applications such as spacecraft tumbling detection, propeller speed control etc. Linear motion detection and linear velocity estimation has been exploited vastly. But, when it comes to angular velocity estimation of a randomly moving object with varying angular speeds, computation is very complex. Hence, computation of AV by non-contact based methods are in huge demand. The need for detection of the direction and value of rotation of a tumbling spacecraft in the Earth's orbit is a huge necessity. Accurate AV estimation has applications in spacecraft docking in space. In the proposed work we have developed an improved algorithm for real-time estimation of rotation using live video feed. The AV of the object can be measured by the camera feed of the rotating bodies as grabbed by the video camera. Angle is computed on every frame with respect to a reference and monitored for a certain number of frames to give the instantaneous AV. The number of frames processed by the camera is dynamic and is estimated prior to the angle calculation. Comparison of performance of AV estimation by the proposed algorithm with state-of-the-art Lucas Kanade(LK) object tracking method based AV estimation is also reported. The proposed approach shows significant improvement in estimating velocities whose motion is circular in nature. Additionally, this work advocates a novel approach towards an effective real-time angular velocity calculation by thresholding techniques, blob identification, and centroid detection.

Laser Doppler anemometry offers a non-intrusive in-situ flow measurement method both for scientific and industrial environments, especially in extreme conditions. Compared to the commercial LDA systems Nano-LDA device was developed for simultaneous flow measurement, particle counting and sizing down to the nanometer size range. High detection sensitivity was reached by applying single photon avalanche diodes with photon correlation technique and special techniques for particle counting (burst selecting) and individual burst signal processing. In this paper verifying measurements are performed with Palas 2.0 iP aerosol generator and differential mobility analyzer down to 75nm paraffin particles, which is in accord with the lower size limit of the generator. In case of individual particle velocity estimations the low SNR signal requires special prepare of the autocorrelation function such as unfolding, zero padding and windowing. A detailed discussion is shown for the role of the different techniques in velocity estimation. The amplitude technique in the particle sizing requires a calibration process to determine the intensity loss of the system. The model-based algorithm supports the calibration by the complete simulation of the measurement process and light scattering. By this way a single calibration measurement for one kind of monodisperse particles can be enough. The model-based algorithm is tested by measurements with monodisperse particles of different sizes set by the DMA.

Attitude and angular velocity estimation is essential for various applications of the user equipment (UE). In gyroscope-free scenarios, how to perform attitude and angular velocity estimation is a critical issue. In this letter, we propose a UE attitude and angular velocity estimation framework with the assistance of channel state information (CSI) variation. We first present a detailed proof of the existence of the mapping from CSI variation to angular velocity. Furthermore, we employ a neural network to learn this mapping, leveraging its capacity to model complex relationships. Then the estimated angular velocity is incorporated into the kinematic model of the gyroscope to derive the current attitude estimate of the UE. Simulation results further substantiate the significant potential of our method as a viable alternative to gyroscopes. Moreover, results also show that more propagation paths leads to better angular velocity and attitude estimation.

Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.

Kinematic information such as position, velocity, and acceleration is critical to determine the three-dimensional state of the robot in space. In this study, it is aimed to estimate as visual the linear and angular velocity of a mobile robot. Additionally, another aim of this study is to determine the suitability of the concatenation or subtraction layer in the Convolutional Neural Network (CNN) that will make this estimate. For these purposes, first, a simulation environment was created. 9000 pairs of images and necessary velocity information were collected from this simulation environment for training. Similarly, 1000 pairs of images and velocity information were gathered for validation. Four different CNN models were designed and these models were trained and tested using these datasets. As a result of the test, the lowest average error for linear velocity estimation was calculated as 0.93e-3m/s and angular velocity estimation was measured as 4.37e-3rad/s. It was observed that the results were sufficient for linear and angular velocity prediction according to statistical analysis of errors. In addition, it was observed that the subtraction layer can be used instead of the concatenation layer in the CNN architectures for hardware-limited systems. As a result, visual velocity estimation of mobile robots has been achieved with this study and the framework of CNN models has been drawn for this problem.

Slip plays a vital role in traction control when a mobile robot traverses over soft soils. To estimate slip parameters, accurate measurement of robot velocity is particularly required. Previous related work done by the authors has adopted a single downward-looking single camera system for velocity and slip estimation [1][2]; however, such a single camera system is prone to lose accuracy when the distance between the camera and terrain is time-varying, such as traversing over uneven terrains [1]. To cope with the problem, this paper presents a robust downward-looking camera based velocity estimation approach, which can particularly be capable of identifying height variation and compensating for velocity estimation. A downward-looking stereo camera instead of previously used single camera is adopted. The camera-terrain distance can be estimated by matching same features in left and right frames. Robot velocity measured with height compensation can be more accurate, compared to estimates without it. The proposed approach has been validated through comprehensive experimental study on a lab-based test rig; and test results show good performance of the proposed approach. With the proposed method, slip estimation techniques given by [2][3] can be promisingly extended to non-flat terrains.

In this work, a sensorless control scheme was designed for permanent magnet synchronous motors based on a sampled model. The discrete-time model was obtained using the Symplectic Euler method. Taking such model along with voltage and current measurements, an observer is designed for rotor position and velocity estimation. Then, the rotor velocity is forced to track a desired reference signal by means of a discrete-time quasi-sliding mode technique.

Vision-based ego-velocity estimation is a fundamental problem in robot state estimation. However, the constraints of frame-based cameras, including motion blur and insufficient frame rates in dynamic settings, readily lead to the failure of conventional velocity estimation techniques. Mammals exhibit a remarkable ability to accurately estimate their ego-velocity during aggressive movement. Hence, integrating this capability into robots shows great promise for addressing these challenges. In this letter, we propose a brain-inspired framework for linear-angular velocity estimation, dubbed NeuroVE. The NeuroVE framework employs an event camera to capture the motion information and implements spiking neural networks (SNNs) to simulate the brain's spatial cells' function for velocity estimation. We formulate the velocity estimation as a time-series forecasting problem. To this end, we design an Astrocyte Leaky Integrate-and-Fire (ALIF) neuron model to encode continuous values. Additionally, we have developed an Astrocyte Spiking Long Short-term Memory (ASLSTM) structure, which significantly improves the time-series forecasting capabilities, enabling an accurate estimate of ego-velocity. Results from both simulation and real-world experiments indicate that NeuroVE has achieved an approximate 60% increase in accuracy compared to other SNN-based approaches.

A method for parameter estimation is presented using the Kalman filter with appropriate initial conditions. The filter solution is shown to approximate the minimum-norm weighted least-squares solution to any desired accuracy during all phases of estimation. Furthermore, the computations are identical for each measurement, irrespective of whether a minimal observable data set has been established. This procedure contrasts with other techniques for parameter estimation that require additional computation when the process is unobservable.

The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.

Center of mass estimation during non-cyclic activities: Comparison of marker-based methods and their fusion with ground reaction forces.

A method of mobile position tracking and velocity estimation for network-based mobile positioning systems is presented. Kalman filtering is used to process the sequences of mobile location measures obtained from time-of-arrival (TOA) geometric location techniques. Two-dimensional position tracking and velocity estimation are obtained. Simulation results show that carefully adjusting the processing rate of Kalman filtering can reduce the location tracking error. One post-Kalman-filter smoothing method is also proposed for better performance of velocity estimation.

The article proposes an integrated approach to modeling the initial stage of tracking air targets in radar systems based on a combination of a probabilistic-temporal model of the GERT type and a recursive state estimation procedure using the Kalman filter. At the macro level, the process of interaction "radar-tracked object" is described by an exponential GERT network using moment generating functions, which allows analytically forming an equivalent W-function, determining the distribution and probability density functions of the interaction time, as well as taking into account multivariate transition trajectories, returns to previous states, and detection confirmation delays. It is shown that the GERT model effectively reproduces global temporal-probabilistic regularities of the process, but does not provide recursive correction of coordinates and velocities estimates in real time and minimization of the mean square error. To overcome these limitations, it is proposed to integrate the Kalman filter as a micro-level tool for trajectory estimation. A discrete linear stochastic model in the state space is formulated, a two-step recursion "prediction-correction", principles of selection of motion models (CV, CA, CT), structures of noise covariance matrices and tuning procedures based on RMSE and NEES criteria are presented. It is shown that the time characteristics obtained from GERT can be used to initialize the initial state and covariances, as well as for adaptive tuning of the sampling step and process noise parameters. The modeling results demonstrate the complementarity of the approaches: GERT forms a structural-probabilistic framework of the tracking process, while the Kalman filter provides smoothing of measurements, reduction of the spread of estimates and a significant reduction of RMSE compared to the macro-level estimate. The proposed integration framework "GERT → Kalman" creates a methodological basis for building a comprehensive model of goal tracking in a cluttered environment, combining the analysis of the temporal-probabilistic structure of the process with highly accurate recursive real-time state estimation.

A gait velocity estimation algorithm using the inertial sensors of a smartwatch is proposed. The peaks of accelerometer and gyroscope norms are detected at first. Then a Kalman Filter is employed to recover the peaks that are missed because of the arm swing. The Kalman filter combines the accelerometer and gyroscope norm peaks and robustly detect walking step events even in cases where there is a large arm swing. Walking velocity is then estimated using the step duration. It will be shown in this work that the gait velocity has a good correlation with the inverse of the square of the step duration. The model parameters are calculated by collecting the training data from 25 subjects: each subject walked 50 m six times with different walking speed and different arm swing speed. The standard deviation of walking velocity estimation error is 0.1009 m/s (without person dependent calibration) and 0.0630 m/s (with person dependent calibration). The average precision of 91.7% was achieved for the gait speed testing on the smartwatch platform over all the speed scenarios.