Speed Estimation Error Research Articles

Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles

<abstract><p>Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.</p></abstract>

Sensorless control of the BPMSM for artificial heart based on the improved SMO, the improved HFI and the GAPSO algorithm

BackgroundIncrease in volume in the bearingless permanent magnet slice motor (BPMSM) control system for artificial hearts is the major problem in the existing works. ObjectiveA sensorless control method of the BPMSM for the artificial heart is proposed in the study based on an improved sliding mode observer (SMO) and improved high-frequency injection method. Also, a full-speed rotor position detection method combining the high-frequency injection (FHI) method and sliding mode observer is designed. MethodologyIn this method, an improved high-frequency injection method was designed in the low-speed domain, and an improved sliding-mode observer was designed in the medium–high-speed domain. Besides, the speed domain switching method based on the genetic particle swarm optimization (GAPSO) algorithm was adopted at the critical point of the low-speed domain and the medium and high-speed domain to realize the smooth switching between different estimation methods, and then realize the sensorless control of the rotor in the full speed domain. Secondly, the simulation study by Simulink was used to compare the detection effects of the BPMSM rotor position and speed of the artificial heart pump under different methods are compared in this work. ResultThe results show that the speed estimation value and rotor position estimation value of the new method in this paper were closer to the actual value, the position estimation error is 3%, and the speed estimation error was within 1.5%. Further, the experimental study was carried out on the principal prototype of the sensorless control part of the artificial heart to verify the effectiveness of the proposed method. ConclusionThe method proposed in this paper has certain generality in the field of motor sensorless control technology, and has important reference value for researchers in this field.

Sensorless Control of Variable-Speed SCIG Wind Energy Conversion Systems Based on Rotor Flux Estimation Using ROGI-FLL

In this article, a novel sensorless control method for variable-speed squirrel-cage induction generator (SCIG) wind power systems with simple rotor flux estimation is proposed. The rotor flux linkages are estimated using a reduced-order generalized integrator (ROGI)-frequency-locked loop (FLL), which regards the rotor back electromotive forces (EMFs) found from the generator model as its inputs. Based on these rotor flux linkages, the rotor flux angle can be estimated through direct trigonometric relations. Then, to avoid the speed estimation error caused by the FLL under stator frequency variations, the synchronous speed is estimated from the aforementioned rotor flux angle via the derivative. Owing to the excellent harmonic rejection capability of the ROGI, the proposed rotor flux estimation is robust against measured high-frequency ripple components. The analysis of ROGI response in the presence of dc offset in the rotor back EMFs is provided as well, and it shows that the proposed method is not faced with the saturation under those conditions. Performance comparisons between the proposed method and the existing dual second-order generalized integrator (SOGI)-FLL-based method have been conducted via both simulations and experiments, thereby verifying the feasibility of the proposed method.

IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

The violation traffic laws by driving at high speeds, the overloading of passengers, and the unfastening of seatbelts are of high risk and can be fatal in the event of any accident. Several systems have been proposed to improve passenger safety, and the systems either use the sensor-based approach or the computer-vision-based approach. However, the accuracy of these systems still needs enhancement because the entire road network is not covered; the approaches utilize complex estimation techniques, and they are significantly influenced by the surrounding environment, such as the weather and physical obstacles. Therefore, this paper proposes a novel IoT-based traffic violation monitoring system that accurately estimates the vehicle speed, counts the number of passengers, and detects the seatbelt status on the entire road network. The system also utilizes edge computing, fog computing, and cloud computing technologies to achieve high accuracy. The system is evaluated using real-life experiments and compared with another system where the edge and cloud layers are used without the fog layer. The results show that adding a fog layer improves the monitoring accuracy as the accuracy of passenger counting rises from 94% to 97%, the accuracy of seatbelt detection rises from 95% to 99%, and the root mean square error of speed estimation is reduced from 2.64 to 1.87.

Speed Estimation of PMSM Based on a Super-Twisting Slide Mode Observer

To limit the high-frequency chattering originating from the conventional sliding mode observer in a PMSM sensorless control system of vacuum pumps and reduce the steady-state error caused by the phase lag of the coordinate system on the estimated speed, a speed estimation algorithm was proposed based on the super-twisting sliding mode observer. Then, the proposed algorithm was applied to observe an electromotive force. To this end, the Lyapunov function was established to analyze the stability of the current dynamic error equation. In this regard, the estimated phase lag of the rotating coordinate system was considered to study the influence of the speed estimation error. Moreover, a speed estimation algorithm considering the d-axis estimation of back electromotive force is proposed. The performed analyses show that the speed estimation algorithm based on a super-twisting sliding mode observer has promising estimation accuracy. It is concluded that the proposed algorithm can be effectively applied to suppress high-frequency chattering in the system, reduce the influence of phase lag in the estimation coordinate system on the speed estimation error, remove the low-pass filter and phase compensation module, and improve the accuracy of speed estimation.

An Improved Permanent Magnet Synchronous Motor Rotor Position Observer Design Based on Error Harmonic Elimination

An improved rotor position observer based on sliding mode control was proposed to eliminate estimation error and harmonic ripples for PMSM sensorless control. A multi-proportional resonant filter was designed instead of a low-pass filter to filter out the specific harmonics of back EMF. The improved filter can solve the phase delay problem caused by a low-pass filter, eliminate the rotor angle compensator, simplify the system structure, and effectively suppress the system chattering. Then, a fractional normalized phase-locked loop was adopted to calculate the speed and rotor position, further eliminating the influence of noise. Compared with the traditional sliding mode control and the improved sliding mode control, the improved sliding mode observer can effectively suppress the back EMF chattering and reduce the speed estimation error. The correctness and effectiveness of the proposed improved sliding mode observer were verified through the simulation model and experimental platform of PMSM sensorless control.

Rethinking Doppler Effect for Accurate Velocity Estimation With Commodity WiFi Devices

Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate targets’ velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.38cm/s$ </tex-math></inline-formula> , 13.42°, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$31.08cm$ </tex-math></inline-formula> median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.

Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

AbstractModel‐based speed estimation schemes in sensorless field‐oriented control (FOC) of induction machines (IMs) are designed on the basis of a corresponding mathematical model. Thus, a reliable and robust sensorless controller requires using a more detailed IM model. The omission of nonlinear phenomena in real‐time application, such as magnetic saturation, iron losses, and stray‐load losses, certainly leads to inaccuracy in the estimated speed due to the existence of the mentioned phenomena in the IM. In this context, we address here the problem of neglecting the stray‐load and iron losses in a model‐based speed observer, synthesized by means of sliding mode theory, and we evaluate the impact of this omission on the execution of the utilized FOC and maximum power point tracking (MPPT) algorithm applied for a stand‐alone wind energy conversion system (WECS). The performance of the proposed sliding mode speed observer (SMSO), derived from the IM model including the above mentioned nonlinear phenomena, is experimentally evaluated over wide ranges of wind speed and IM magnetizing flux. It is also compared with the model reference adaptive system (MRAS) speed estimator, which is derived from the same IM model, as well as to the simplified SMSO, designed based on the IM model without stray‐load and iron losses. The obtained results show that the proposed SMSO has greater robustness with respect to abrupt variations of the rotor flux in comparison with the MRAS speed estimator, whereas it ensures significantly more stable operation and lower speed estimation error in comparison with the simplified SMSO.

Stability Analysis and Trajectory Design of a Nonlinear Switching System for Speed Sensorless Induction Motor Drive

For wide-speed range operation of speed sensorless induction motor drives (SSIMD), the stable operation at low synchronous speed range (including zero synchronous speed) is a great challenge. Virtual voltage injection method is found efficient to overcome this challenge. However, the injected virtual voltage into observer results in the estimated speed error, it is reasonable to rely upon this method only when the performance of traditional SSIMD is poor, i.e., at low speed range. At middle and high speed ranges, virtual voltage injection method cannot meet the high-accuracy requirement of speed estimation in industrial application. For wide-speed range operation of SSIMD, a necessary switching system is proposed in this article to meet the stability requirement and accuracy requirement at the same time. The input-state stability theory is adopted to verify the stability of switching method, because the switching system is a nonlinear and strong coupling system. Then the switching trajectory is optimized for smooth and fast switching transition. Finally, the effectiveness and feasibility of the proposed method are verified by experimental results.

Speed Estimation Method of Linear Motor Extended Kalman Filter Based on Attenuation Memory

In allusion to the phenomenon that the extended Kalman filter is easy to diverge in the mover position estimation of permanent magnet synchronous linear motor, a linear motor extended Kalman filter speed estimation method based on attenuation memory is designed. By setting the attenuation factor, α, the extended Kalman filter is introduced to increase the weight of the latest speed data and restrain the divergence of the filter, so as to achieve a better speed tracking effect. In the simulation experiment of the sensorless control of a linear motor, the AMEKF algorithm can significantly improve the speed estimation accuracy of standard EKF, and the speed estimation error is reduced by 0.75%. At the same time, it still maintains a good speed tracking effect and good dynamic performance under variable speed and different load conditions.

An Extension of Adaptive State Observers Using Dynamic Regressor Extension and Mixing With Relaxed Excitation Conditions

The recently proposed DREM-based adaptive state observer (DREMBAO) design approach assumes that there exists transformation of the system model into a cascade form, linearly dependent on parameters vector and depends only on measured input and output. This paper proposes an extension to relax that assumption and extend the approach to state affine systems. Like the original version of DREMBAO, the proposed approach ensures global convergence of an estimation error to zero under a relatively weak condition. The advantages of the extension are demonstrated in the example with a permanent magnet synchronous motor. In contrast to the original approach, the proposed extension allows taking into account viscous friction in the motor and obtaining a simplified load torque estimation algorithm. Moreover, finite-time convergence to zero is ensured for the load torque and speed estimation error under a weak interval excitation.

Experimental Studies on a Spinner Anemometer Using Pressure Sensor by a Field Wind Turbine

For the wind assessment at the wind turbine performance test, a meteorological mast at the hub height is installed around 2.5 times the rotor diameter upstream from the wind turbine. However, in recent years, wind turbines have become larger and the construction cost of meteorological mast has also increased. Therefore, in this study, devised a technology to estimate the inflow wind speed is developed by measuring the surface pressure with a pressure sensor mounted on the spinner of the wind turbine rotor and converting it to the inflow wind speed. This technology has the advantage that it can be performed at a lower cost than the existing inflow observation technology. The purpose of this paper is to clarify the application of spinner-mounted pressure sensors in natural environments through field experiments. The obtained wind speed by the pressure on the spinner was compared to the inflow wind speed measured by an ultrasonic anemometer located upstream of the wind turbine. As a result, the spinner sensor wind speed estimation error may exceed the nominal error range equivalent to ±10 Pa of pressure sensor accuracy. However, the spinner sensor wind speed can generally estimate the inflow wind speed.

Three-Phase Induction Motor Speed Estimation Using Recurrent Neural Network Structure

In induction motor speed control method, the development of the field-oriented control (FOC) algorithm which can control torque and flux separately enables the motor to replace many roles of DC motors. Induction motor speed control can be done by using a close loop system which requires a speed sensor. Referring to the speed sensor weaknesses such as less accurate of the measurement, this is due to the placement of the sensor system that is too far from the control system. Therefore, a speed sensorless method was developed which has various advantages. In this study, the speed sensorless method using an artificial neural network with recurrent neural network (RNN) as speed observer on three-phase induction motor has been discussed. The RNN can maintain steady-state conditions against a well-defined set point speed, so that the observer is able and will be suitable if applied as input control for the motor drives. In this work, the RNN has successfully estimated the rotor flux of the induction motor in MATLAB R2019a simulation as about 0.0004Wb. As based on speed estimation error, the estimator used has produced at about 26.77%, 8.7% and 6.1% for 150rad/s, 200rad/s and 250rad/s respectively. The future work can be developed and improved by creating a prototype system of the induction motor to get more accurate results in real-time of the proposed RNN observer.

A Simple Approach for Real Time Speed Estimation of On Road Vehicles Using Video Sequences

This paper provides an efficient and simple approach towards real-time speed estimation of on road vehicles for surveillance applications. In the presented method, videos are supposed to be captured with a stationary camera mounted on a two-lane road and there is no need for the camera to be calibrated. The algorithm has two main phases, in the first phase there is an interactive procedure in which lane borders and real world distances are defined just once at the beginning. Then, based on the already received information, two rectangular ROIs are defined for each lane. In the second phase, approximate binary mask of the foreground is created differencing the two consecutive frames. Eventually, calculating centroids and the norm values of the binary mask in the ROIs, algorithm can compute the time that it takes each vehicle to pass between the two aforementioned lines and thus, average speed can be computed. In short, although the algorithm of this paper is simple, it is real-time and efficient, and its implementation doesn’t require any specific hardware. The average error of speed estimation is ±3km/h and the detection accuracy is 83 %.

Comparative Analysis of Two Speed-Estimation Methods for Dual Three-Phase Induction Motor with Stator Resistance Online Identification

The dual three-phase induction motor (DTPIM) has gained wide attention in special applications, such as vessel propulsion, because of its advantages of less torque ripple and higher reliability. However, speed sensors are greatly affected and easily become faulty when used in harsh environments for a long time. In this paper, two model reference adaptive system (MRAS) speed-estimation methods are proposed, based on the double (α, β) coordinate system (DCS) and vector space decomposition method (VSD) of the two groups of the three-phase armature vectors, respectively. Both methods can be used for the speed sensorless control system of the DTPIM to improve reliability. The changing of the stator resistance value, caused by temperature variation, affects the accuracy of the speed-estimation. Two online resistance-identification algorithms, combining the DCS method and the VSD method, were proposed to reduce the effect of changes in stator resistance. Simulation results show that the dynamic speed-estimation error of the VSD method decreased greatly compared with the DCS method, which verifies the effectiveness of the theoretical analysis.

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.

On the benefit of smart tyre technology on vehicle state estimation

Among other variables, smart tyre systems are capable of determining tyre contact forces. Combining this information with the signals obtained from conventional vehicle sensors, e.g. inertial measurement units, wheel speed sensors and steering wheel angle sensors, improves the estimation accuracy of the states used by the vehicle dynamics controllers of production cars. This study assesses the performance improvement brought by the vertical and longitudinal tyre contact force signals, obtained through smart tyres, to an unscented Kalman filter (UKF) for vehicle speed and sideslip angle estimation, based on a nonlinear vehicle dynamics model. Two UKF designs, excluding and including smart tyre information, are compared by using experimental data from a purposely sensorised high-performance passenger car, along a comprehensive set of manoeuvres. The results show average ∼60% and ∼37% reductions of the root mean square values of the normalised vehicle speed and sideslip angle estimation errors for the filter design receiving the smart tyre information. In comparison with the more conventional UKF design without tyre force inputs, the smart tyre based estimator is also characterised by significantly enhanced robustness and adaptability to typical variations of vehicle and tyre parameters, such as the tyre-road friction coefficient.

Pedometer-free Geomagnetic Fingerprinting with Casual Walking Speed

The geomagnetic field has been wildly advocated as an effective signal for fingerprint-based indoor localization due to its omnipresence and local distinctive features. Prior survey-based approaches to collect magnetic fingerprints often required surveyors to walk at constant speeds or rely on a meticulously calibrated pedometer (step counter) or manual training. This is inconvenient, error-prone, and not highly deployable in practice. To overcome that, we propose Maficon, a novel and efficient pedometer-free approach for geo ma gnetic fi ngerprint database con struction. In Maficon, a surveyor simply walks at casual (arbitrary) speed along the survey path to collect geomagnetic signals. By correlating the features of geomagnetic signals and accelerometer readings (user motions), Maficon adopts a self-learning approach and formulates a quadratic programming to accurately estimate the walking speed in each signal segment and label these segments with their physical locations. To the best of our knowledge, Maficon is the first piece of work on pedometer-free magnetic fingerprinting with casual walking speed. Extensive experiments show that Maficon significantly reduces walking speed estimation error (by more than 20%) and hence fingerprint error (by 35% in general) as compared with traditional and state-of-the-art schemes.

A new speed adaptive estimation method based on an improved flux sliding-mode observer for the sensorless control of PMSM drives

In this paper, an improved speed adaptive flux sliding-mode observer (FSMO) is presented for the sensorless control of surface-mounted permanent magnet synchronous motor (PMSM) drives. First, to suppress the chattering of the traditional FSMO, an algorithm for online adjusting the observer gain is designed. In this algorithm, a transition mode is introduced between the existing reaching and sliding modes, and an error criterion function with speed and current is constructed for the three motion modes. Second, to reduce the rotor speed estimation error, a new speed adaptive estimation method is presented instead of the traditional phase-locked loop (PLL). In this method, the adaptive law for estimating the speed is derived using Lyapunov stability and model reference adaptive theories. Meanwhile, the physical meaning of the speed adaptive law is illustrated. Finally, the proposed FSMO scheme is implemented on a TMS320F28335 digital signal processing (DSP) microcontroller-based surface-mounted PMSM sensorless control experimental bench. Test results demonstrate the excellent dynamic performance, robustness and stability of the proposed FSMO under various operating conditions.

Sensorless rotor position estimation of PMSM for low and high rotor speed

In this paper, a sensorless permanent magnet synchronous motor (PMSM) drive was presented based on calculating the back EMF using the reference stator voltages, which are the output of the d-axis current controller, which includes information about the rotor position. Sensorless control estimates the position information necessary for vector control of the motor without using position sensor or encoder. This can provide effects such as increased system reliability and cost and volume reduction. The back EMFs are calculated in a new coordinate based on the estimated speed to minimize the rotor speed estimation error. The estimated rotor position and rotor speed may fail to track the real rotor position and real rotor speed in extremely low speed since the position error can be amplified from the current controller output voltage, and this may cause the instability problems of tracking controller. So, a compensator has been added to the detector to overcome this disadvantage. In order to verify the validity of the proposed sensorless control technique, an experiment was performed on the PMSM, and the results showed fast-dynamic response, low ripples in motor’s currents, and good performance in tracking speed and power references.