Low adhesion detection and identification in a railway vehicle system using traction motor behaviour

It is important to monitor the wheel-rail friction coefficient in railway vehicles to improve their traction and braking performance as well as to reduce the number of incidents caused by low friction. Model based fault detection and identification (FDI) methods, especially state observers have been commonly used in previous research to monitor the wheel-rail friction. However, the previous methods cannot provide an accurate value of the friction coefficient and few of them have been validated using experiments. A Kalman filter based estimator is proposed in this research project. The developed estimator uses signals from the traction motor and provides a new and more efficient approach to monitoring the condition of the wheel-rail contact condition. A 1/5 scaled test rig has been built to evaluate the developed method. This rig comprises 2 axle-hung induction motors driving both the wheelsets of the bogie through 2 pairs of spur gears. 2 DC generators are used to provide traction load to the rollers through timing pulleys. The motors are independently controlled by 2 inverters. Motor parameters such as voltage, current and speed are measured by the inverters. The speed of the wheel and roller and the output of the DC generator are measured by incremental encoders and Hall-effect current clamps. A LabVIEW code has been designed to process all the collected data and send control commands to the inverters. The communication between the PC and the inverters are realized using the Profibus (Process Field Bus) and the OPC (Object Linking and Embedding (OLE) for Process Control) protocol. 3 different estimators were first developed using computer simulations. Kalman filter and its two nonlinear developments: extended Kalman filter (EKF) and unscented Kalman filter (UKF) have been used in these 3 methods. The results show that the UKF based estimator can provide the best performance in this case. The requirement for measuring the roller speed and the traction load are also studied using the UKF. The results show that it is essential to measure the roller speed but the absence of the traction load measurement does not have significant impact on the estimation accuracy. A re-adhesion control algorithm, which reduces excessive creepage between the wheel and rail, is developed based on the UKF estimator. Accurate monitoring of the friction coefficient helps the traction motor work at its optimum point. As the largest creep force is generated, the braking and accelerating time and distance can be reduced to their minimum values. This controller can also avoid excessive creepage and hence potentially reduce the wear of the wheel and rail. The UKF based estimator development has been evaluated by experiments conducted on the roller rig. Three different friction conditions were tested: base condition without contamination, water contamination and oil contamination. The traction load was varied to cover a large range of creepage. The importance of measuring the roller speed and the traction load was also studied. The UKF based estimator was shown to provide reliable estimation in most of the tested conditions. The experiments also confirm that it is not necessary to measure the traction load and give good agreement with the simulation results. With both the simulation and experiment work, the UKF based estimator has shown its capability of monitoring the wheel-rail friction coefficient

EKF and UKF-based estimation of a sensor-less axial flux PM machine under an internal-model control scheme using a SVPWM inverter

This paper presents a comparative estimation study of rotor speed and position of a sensor-less axial flux permanent magnet synchronous motor (AFPMSM) drive system using extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms. An internal model control (IMC) strategy is introduced to control the AFPMSM drive through currents, leading to an extension of PI control with integrators added in the off-diagonal elements to remove the cross-coupling effects between the applied voltages and stator currents in a feed-forward manner. The reference voltage is applied through a space vector pulse width modulation (SVPWM) unit. A diverse set of test scenarios has been realized to comparatively evaluate the state estimation of the sensor-less AFPMSM drive performances under the implemented IMC-based control regime using a SVPWM inverter. The resulting MATLAB simulation outcomes in the face of no-load, nominal load and speed reversal clearly illustrate the well-behaved performances of the two estimation algorithms. The UKF seems to be more promising under noisy conditions. Nevertheless, there is no clear preference for either where steady-state performance is more critical

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications

Vector Control of Asynchronous Motor of Drive Train Using Speed Controller H∞

This study proposes the speed control of an asynchronous motor (AM) using the Antiwindup design. First, the conventional vector control based on proportional-integral (PI) controllers is developed for a constant speed set point. Then, a driving cycle is based on measurements on the Safi/Rabat motorway in Morocco using a microcontroller equipped with a GPS device. The collected practical speed is used as a speed reference for conventional vector control. The /Antiwindup controller of the direct rotor flow-oriented control is used to improve the performance of conventional vector control and optimize the energy consumption of the drive train. The effectiveness of the proposed control scheme is verified by numerical simulation. The results of the numerical validation of the proposed scheme showed good performance compared to conventional vector control. The speed control systems are analyzed for different operating conditions. These control strategies are simulated in the MATLAB/SIMULINK environment. The simulation results of the improved vector control of the Asynchronous Machine (AM) are used to validate this optimization approach in the dynamic regime, followed by a comparative analysis to evaluate the performance and effectiveness of the proposed approach. A practical model based on a TMS320F28379D embedded board and its reduced voltage inverter (24V) is used to implement the proposed method and verify the simulation results. Doi: 10.28991/ESJ-2022-06-04-012 Full Text: PD

Maximising Utilisation of the DC-Link Voltage in the Field Weakening Region for AC Motor Drives

PhD ThesisMost standard electric drives have two operational regions: the constant torque region and field weakening region. In order to increase the power level at the field-weakening region, the phase voltage must be increased. The phase voltage, however, is a function of the inverter input voltage and the control scheme that is applied to the inverter. Several methods have been applied to optimise the stator voltage modulation to maximise the power level at the field-weakening region. These methods suffer from fake voltage extension, which produce high current ripples, and a step reduction of motor currents in the transient area from the constant torque region to the field-weakening region. Adding extra regulators for these methods was proposed, but this still would not show any significant improvement in electric drive performance and increase the additional complexity of the closed-loop control system. During the course of this research, several control schemes based on mathematical modelling and voltage feedback mechanism are proposed to tackle the aforementioned issues. In the proposed novel methods, flux-producing current is designed based on the position of the stator voltage vector to push the stator voltage to the hexagonal voltage boundary. This consequently causes a smooth transition from the constant torque region to the field-weakening region, and it also increases the output torque and power of the electric machine without applying extra controllers or producing a step reduction on the d-axis current. The capabilities of the proposed schemes have been evaluated and compared to conventional model-based and closed-loop voltage algorithms by using MATLAB simulation and an experimental test set-up. This research also developed and proposed two parameter estimation techniques based on EKF and combined MRAS-KF to improve the accuracy of online estimation techniques. The performance of developed estimation schemes was investigated by using MATLAB simulation and a plant emulator-based setup

Investigations on Direct Torque and Flux Control of Speed Sensorless Induction Motor Drive

The Induction motors (IM) are used worldwide as the workhorse in most of the industrial applications due to their simplicity, high performance, robustness and capability of operating in hazardous as well as extreme environmental conditions. However, the speed control of IM is complex as compared to the DC motor due to the presence of coupling between torque and flux producing components. The speed of the IM can be controlled using scalar control and vector control techniques. The most commonly used technique for speed control of IM is scalar control method. In this method, only the magnitude and frequency of the stator voltage or current is regulated. This method is easy to implement, but suffers from the poor dynamic response. Therefore, the vector control or field oriented control (FOC) is used for IM drives to achieve improved dynamic performance. In this method, the IM is operated like a fully compensated and separately excited DC motor. However, it requires more coordinate transformations, current controllers and modulation schemes. In order to get quick dynamic performance, direct torque and flux controlled (DTFC) IM drive is used. The DTFC is achieved by direct and independent control of flux linkages and electromagnetic torque through the selection of optimal inverter switching which gives fast torque and flux response without the use of current controllers, more coordinate transformations and modulation schemes. Many industries have marked various forms of IM drives using DTFC since 1980. The linear fixed-gain proportional-integral (PI) based speed controller is used in DTFC of an IM drive (IMD) under various operating modes. However, The PI controller (PIC) requires proper and accurate gain values to get high performance. The PIC gain values are tuned for a specific operating point and which may not be able to perform satisfactorily when the load torque and operating point changes. Therefore, the PIC is replaced by Type-1 fuzzy logic controller (T1FLC) to improve the dynamic performance over a wide speed range and also load torque disturbance rejections. The T1FLC is simple, easy to implement and effectively deals with the nonlinear control system without requiring complex mathematical equations using simple logical rules, which are decided by the expert. In order to further improve the controller performance, the T1FLC is replaced by Type-2 fuzzy logic controller (T2FLC). The T2FLC effectively handles the large footprint of uncertainties compared to the T1FLC due to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, whereas the T1FLC consists only a Type-1 fuzzy sets and single membership function. The training data for T1FLC and T2FLC is selected based on the PIC scheme

Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles

Sensorless control of Electric Vehicle (EV) drives is considered to be an effective approach to improve system reliability and to reduce component costs. In this paper, relevant aspects relating to the sensorless operation of EVs are reported. As an initial contribution, a hybrid sensorless control algorithm is presented that is suitable for a variety of synchronous machines. The proposed method is simple to implement and its relatively low computational cost is a desirable feature for automotive microprocessors with limited computational capabilities. An experimental validation of the proposal is performed on a full-scale automotive grade platform housing a 51¿kW Permanent Magnet assisted Synchronous Reluctance Machine (PM-assisted SynRM). Due to the operational requirements of EVs, both the strategy presented in this paper and other hybrid sensorless control strategies rely on High Frequency Injection (HFI) techniques, to determine the rotor position at standstill and at low speeds. The introduction of additional high frequency perturbations increases the power losses, thereby reducing the overall efficiency of the drive. Hence, a second contribution of this work is a simulation platform for the characterization of power losses in both synchronous machines and a Voltage Source Inverters (VSI). Finally, as a third contribution and considering the central concerns of efficiency and autonomy in EV applications, the impact of power losses are analyzed. The operational requirements of High Frequency Injection (HFI) are experimentally obtained and, using state-of-the-art digital simulation, a detailed loss analysis is performed during real automotive driving cycles. Based on the results, practical considerations are presented in the conclusions relating to EV sensorless control.Peer ReviewedPostprint (published version

Innovative Observers for Induction Motor Control

In this thesis the problem of designing observers and controllers for induction motors has been studied and thoroughly discussed using tools from the nonlinear system theory, both for analysis and control purposes. Useful techniques have been used to identify the machine parameters off-line. In the Introduction was dealt the design a control system for a drive of asynchronous machines that uses a voltage source inverter to generate the currents and voltages which carry the drive by making use of an observer of the state rotor variables. The used control algorithms is the \textit{field oriented control} (FOC). The model of the asynchronous induction motor is described starting from the mechanical model, the electrical model through the Steinmetz equivalent electrical circuit that is the IEEE recommended equivalent circuit, the State Model of Asynchronous Motor and the mathematical model, completing with the discretization of the last model, realizing the Discrete time mathematical model. In Chapter 1 several observer are taken in account in order to be able to compare their strengths and weaknesses. At first it's developed a (FOLO) Full Order Luemberger Observer and its Reduced Order version. Other observer that was considered it's the Sliding one. Then a version of Non linear Flux observer was synthesized in order to consider the effects of saturation which introduce a nonlinear effects. At last the (EKF) Extended Kalman Filter was considered, both in complex (ECKF) and the (RAKF) Robust Adaptive Kalman Filter version. In Chapter 2 have been presented experimental and simulation results obtained by testing each of the previous algorithms. Excellent results were obtained by use of observer based on Kalman Filter, and in particular, the Extended Complex Kalman Filter, because no matrix inversion is required. In fact the operation of matrix inversion that is necessary in the classic Extended Kalman Filter is therefore translated in the inverse of a real number in the proposed Extended Complex Kalman Filter. In conclusion, a set of tools present in control theory have been applied successfully in motion control systems with induction motors. This work is certainly not a complete treatment, since many basic parts are omitted (only the references are given), and for this reasons is to be understood as completion of studies related to the subject matter

Modelling and Control of Stepper Motors for High Accuracy Positioning Systems Used in Radioactive Environments

Hybrid Stepper Motors are widely used in open-loop position applications. They are the choice of actuation for the collimators in the Large Hadron Collider, the largest particle accelerator at CERN. In this case the positioning requirements and the highly radioactive operating environment are unique. The latter forces both the use of long cables to connect the motors to the drives which act as transmission lines and also prevents the use of standard position sensors. However, reliable and precise operation of the collimators is critical for the machine, requiring the prevention of step loss in the motors and maintenance to be foreseen in case of mechanical degradation. In order to make the above possible, an approach is proposed for the application of an Extended Kalman Filter to a sensorless stepper motor drive, when the motor is separated from its drive by long cables. When the long cables and high frequency pulse width modulated control voltage signals are used together, the electrical signals difer greatly between the motor and drive-side of the cable. Since in the considered case only drive-side data is available, it is therefore necessary to estimate the motor-side signals. Modelling the entire cable and motor system in an Extended Kalman Filter is too computationally intensive for standard embedded real-time platforms. It is, in consequence, proposed to divide the problem into an Extended Kalman Filter, based only on the motor model, and separated motor-side signal estimators, the combination of which is less demanding computationally. The efectiveness of this approach is shown in simulation. Then its validity is experimentally demonstrated via implementation in a DSP based drive. A testbench to test its performance when driving an axis of a Large Hadron Collider collimator is presented along with the results achieved. It is shown that the proposed method is capable of achieving position and load torque estimates which allow step loss to be detected and mechanical degradation to be evaluated without the need for physical sensors. These estimation algorithms often require a precise model of the motor, but the standard electrical model used for hybrid stepper motors is limited when currents, which are high enough to produce saturation of the magnetic circuit, are present. New model extensions are proposed in order to have a more precise model of the motor independently of the current level, whilst maintaining a low computational cost. It is shown that a significant improvement in the model It is achieved with these extensions, and their computational performance is compared to study the cost of model improvement versus computation cost. The applicability of the proposed model extensions is demonstrated via their use in an Extended Kalman Filter running in real-time for closed-loop current control and mechanical state estimation. An additional problem arises from the use of stepper motors. The mechanics of the collimators can wear due to the abrupt motion and torque profiles that are applied by them when used in the standard way, i.e. stepping in open-loop. Closed-loop position control, more specifically Field Oriented Control, would allow smoother profiles, more respectful to the mechanics, to be applied but requires position feedback. As mentioned already, the use of sensors in radioactive environments is very limited for reliability reasons. Sensorless control is a known option but when the speed is very low or zero, as is the case most of the time for the motors used in the LHC collimator, the loss of observability prevents its use. In order to allow the use of position sensors without reducing the long term reliability of the whole system, the possibility to switch from closed to open loop is proposed and validated, allowing the use of closed-loop control when the position sensors function correctly and open-loop when there is a sensor failure. A different approach to deal with the switched drive working with long cables is also presented. Switched mode stepper motor drives tend to have poor performance or even fail completely when the motor is fed through a long cable due to the high oscillations in the drive-side current. The design of a stepper motor output fillter which solves this problem is thus proposed. A two stage filter, one devoted to dealing with the diferential mode and the other with the common mode, is designed and validated experimentally. With this ?lter the drive performance is greatly improved, achieving a positioning repeatability even better than with the drive working without a long cable, the radiated emissions are reduced and the overvoltages at the motor terminals are eliminated

High performance sensorless vector control of induction motor drives

The aim of this research project was to develop a vector controlled induction motor drive operating without a speed or position sensor but having a dynamic performance comparable to a sensored vector drive. The methodology was to detect the motor speed from the machine rotor slot harmonics using digital signal processing and to use this signal to tune a speed estimator and thus reduce or eliminate the estimator’s sensitivity to parameter variations. Derivation of a speed signal from the rotor slot harmonics using a Discrete Fourier Transform-based algorithm has yielded highly accurate and robust speed signals above machine frequencies of about 2 Hz and independent of machine loads. The detection, which has been carried out using an Intel i860 processor in parallel with the main vector controller, has been found to give predictable and consistent results duing speed transient conditions. The speed signal obtained from the rotor slot harmonics has been used to tune a Model Reference Adaptive speed and flux observer, with the resulting sensorless drive operating to steady state speed accuracies down to 0.02 rpm above 2 Hz (i.e. 60 rpm for the 4 pole machine). A significant aspect of the research has been the mathematical derivation of the speed bandwidth limitations for both sensored and sensorless drives, thus allowing for quantitative comparison of their dynamic performance. It has been found that the speed bandwidth limitation for sensorless drives depends on the accuracy to which the machine parameters are known and that for maximum dynamic performance it is necessary to tune the flux and speed estimator against variations in stator resistance in addition to the tuning mechanism deriving from the DFT speed detector. New dynamic stator resistance tuning algorithms have been implemented. The resulting sensorless drive has been found to have a speed bandwidth equivalent to sensored drives fitted with medium resolution encoders (i.e. about 500 ppr), and a zero speed accuracy of ± 8 rpm under speed control. These specifications are superior to any reported in the research literature