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Multilayer Control of an Induction Motor Drive: A Strategic Step for Automotive Applications. Hainan Wang, Steve Pekarek, Member, IEEE, and Babak Fahimi, ...



Multilayer Control of an Induction Motor Drive: A Strategic Step for Automotive Applications Hainan Wang, Steve Pekarek, Member, IEEE, and Babak Fahimi, Senior Member, IEEE

Abstract—Fault tolerance is a critical attribute in automotive electrical and propulsion systems. In this paper, a control scheme is presented that allows an induction motor drive system to operate in the event of multiple sensor failures. Automatic diagnosis of sensor fault and recovery is performed and used to reconfigure the drive system controls to achieve the best performance in lieu of component degradation. This approach couples a new digital delta-hysteresis regulation scheme with a model reference adaptive system scheme in order to provide fault tolerance for both phase-current and rotor position (speed) sensors. Simulation and experimental results are provided to show the effectiveness of the proposed scheme. Index Terms—Current regulation, induction drives, model reference adaptive system (MRAS) speed identification, sensor fault diagnosis, sensor fault tolerance.


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NOMENCLATURE Stator current in stationary reference frame. Stator phase currents. Commanded stator phase currents. Stator flux linkages in stationary reference frame. Rotor flux linkages in stationary reference frame. Stator current in synchronous reference frame. Rotor electrical angular velocity (rad/s). Identified rotor electrical angular velocity (rad/s). Slip frequency (rad/s). Frequency of applied stator voltages (rad/s). Angular position of synchronous reference frame. Transformation for stator phase variables (abc) to synchronous reference frame. Transformation for stator phase variables (abc) to stationary reference frame.

I. INTRODUCTION AULT tolerance is a vital attribute of electric drive systems used in numerous automotive subsystems, including propulsion, steering, and braking. The design of fault-tolerant


Manuscript received March 9, 2005; revised October 26, 2005. This work was supported by the Office of Naval Research under ATI Grants N00014–02–1–0623 and N00014–04–1–0815. Recommended by Associate Editor J. Shen. H. Wang is with the Electrical Engineering Department, Pioneer Magnetics, Los Angeles, CA 90066 USA. S. Pekarek is with the Electrical and Computer Engineering, Purdue University,West Lafayette, IN 47907 USA (e-mail: [email protected]). B. Fahimi si with the Power Electronics and Controlled Motion Lab, University of Texas, Arlington, TX 76019 USA (e-mail: fah[email protected]). Digital Object Identifier 10.1109/TPEL.2006.872370


electric drives has received considerable attention in recent years. For example, in [1], the behavior of a voltage-controlled pulse-width modulation (PWM) induction motor drive under semiconductor, dc bus, line-line, and input line-ground faults was studied to provide a means to determine system stress and design protection and fault diagnosis schemes. In [2], the design of a permanent magnet (PM) machine-based drive was considered subject to achieving the best possible performance in cases of winding open- and short-circuit, semiconductor device open- and short-circuit, and dc-link capacitor failure. The behavior of a PM machine operated under open winding and open transistor conditions was also considered in [3]. In [4] the research of [2] was extended through construction and validation of a drive used for aerospace fuel pumps. In [5], a fault-tolerant inverter topology is introduced in which triacs are used to provide an alternative topology in the event of a phase-leg module failure. Inverter topologies that use parallel redundant modules are described in [6]. A comparison of fault-tolerant ac drive topologies is provided in [7]. Most research on fault-tolerance has focused upon dealing with the failure of the power handling components, i.e., the semiconductor devices and machines. However, position sensors and phase current sensors are normally incorporated in high performance drives to provide feedback [8]. In addition, dc-link measurements are normally used for voltage and current protection. Table I shows commonly used sensors. Sensor faults will transfer incorrect feedback signals to the controller and will normally result in the degradation of system performance or even shutdown of the drive [9]. Therefore, the behavior of drives to sensor faults must also be considered when considering fault-tolerance. Several model-based fault diagnosis for schemes have been proposed in [10] and [11], wherein the model of a load is used to predict the occurrence of faults. Under sensor fault conditions, a controller reconfiguration strategy is presented in [12], wherein the regulation scheme of an induction motor drive changes from indirect vector to sensorless vector control when a position sensor fails and to volts/hertz control when phase current sensors fail. Position fault-tolerance using sensorless vector control is reasonable, particularly since the model reference adaptive system (MRAS) method [13], [14] is relatively

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mature and the capability to incorporate more detailed models increases with each new generation of processor. However, switching to volts/hertz control when phase current sensors are lost greatly lowers a drive system’s performance. Specifically, although effective in ensuring continued operation of the machine, volts/hertz control is characterized by a sluggish dynamic behavior and possibly large transients in the motor currents and torque [15]. To address fault tolerant phase current sensors, phase current regulation without phase current sensors is considered herein. A delta-hysteresis regulation (DHR) [16] is commonly applied to regulate phase currents in vector controlled induction motor drives. Within DHR, the phase current sensors are necessary to provide a feedback signal for the current control loop. However, phase current can be reconstructed using knowledge of the dc-link current and switching states. Line-current waveform reconstruction from the dc link current information was presented in [17], wherein an analog circuit was built to determine the motor line currents. The reconstructed phase currents can be used as feedback information for motor control. In [18], a current reconstruction method is derived to provide over-current protection of the motor phases in drives that are operated under space vector modulation. Researchers in [19] present a scheme for direct torque control of ac machines using a single dc link current sensor. Therein, torque and flux are estimated with the reconstructed phase current information and the switchlevel of the inverter is determined by the torque and flux magnitude error. In this research, a discrete delta-hysteresis regulation (DDHR), is used to control the phase current directly using a single dc-link current sensor. Within the scheme, line currents are reconstructed using knowledge of the dc-link current and switching states. The generation of switching signals is based upon traditional DHR. However, modifications are made to DHR to ensure the dc-link current always contains sufficient information to reconstruct phase currents [20]. The research presented in this paper combines the profits of MRAS sensorless vector control and DDHR current regulation and addresses fault diagnosis and tolerance scheme for intermittent or permanent disconnection-type of sensor faults. This scheme automatically detects sensor faults and recoveries and based upon the fault diagnosis, adopts the control algorithm to obtain the best performance from the remaining sensors. Specifically, the dc-link measurements (voltage and current) are served as redundancies when other sensors fail. The position and phase currents are calculated based upon the dc-link measurement and switching states and sent to the controller as feedback information. Experiments using an induction motor drive are used to show that a high level of fault-tolerance is achieved using the proposed technique. Although experimental data is shown for an induction motor drive, a similar approach can be used for PM synchronous machines. II. BACKGROUND A. MRAS Speed Identification The MRAS scheme applied in this research is based upon the comparison of two rotor flux observation models in the stationary reference frame [9]–[14]. Specifically, one model ex-


Fig. 1. Structure of MRAS adaptive speed identification.

presses the rotor flux linkage in terms of stator voltage and stator current



(1) , and

. A second model expresses the rotor flux linkage in terms of stator current and rotor speed

(2) . where The expressions (1) and (2) are two state models for rotor and thus is flux observation. Equation (1) is independent of selected as a reference model. Equation (2) is dependent upon and thus is chosen as an adjustable model. Fig. 1 shows the block diagram of the MRAS speed identification. It is noted that within MRAS an objective function is defined (3) which represents the difference between the reference model and the adjustable model. Rotor speed is estimated by forcing the objective function to zero. A drawback of MRAS sensorless vector control is its low performance at very low speed due to problems associated with pure integration, parameters drifting, and inverter nonlinearities [21]. To improve problems associated with numerical integration, three new algorithms presented in [22] are shown to mitigate speed identification error at low speed. Another approach to improve these problems is to compensate the dc offset using offset identification, correct the voltage distortion using an adaptive nonlinear inverter model, and using online motor parameter identifiers [23], [24]. Although the low speed sensorless control has been an active research area and can be incorporated into the fault-tolerance scheme presented in this paper, the main goal of this investigation is focused on the proof of




Fig. 2. Block diagram of the position sensorless FOC with MRAS identifier and delta-hysteresis current regulation.

feasibility and practicality of the fault-tolerance scheme. Therefore, the performance when continuously operating at low rotor speeds is not considered in detail. It is also noted that the stator phase voltage is required in (1) for reference model flux estimation. However, in a digital signal processor (DSP) controller system, knowledge of the dc link voltage and switching states provides sufficient information to calculate the phase voltage. Specifically, it can be shown that

(4) Fig. 3. Diagram of digital delta-hysteresis current regulation.

represent the inverter switching status for where , , and each phase. Their value is 1 if the upper switch in a phase leg is on and 0 when the lower switch in a phase leg is on. In (4), the effect of inverter dead-time and other inverter nonlinearities such as the voltage drop in PWM inverter is neglected. This will not cause significant error when the drive is continuosly operated at nominal speeds above 50 rpm. However, at very low speed, the voltage distortion due to the inverter nonlinearities will contribute to a severe disturbance [21]. To overcome this problem, methods derived in [22] can be used for stator voltage estimation. Based upon the MRAS scheme described above, a position sensorless field-oriented control (FOC) of an induction machine is configured as shown in Fig. 2. The control system consists of two closed feedback loops. The measured phase currents are fed into MRAS to identify the rotor speed. The estimated speed is used as the feedback signal in the outer speed loop. The inner current feedback loop is realized using a current regulation scheme. Most drives use an additional dc link sensor for over-current protection. B. Digital Delta-Hysteresis Current Regulation For stator current regulation, a delta-hysteresis strategy is commonly applied and has been proven to be effective in controlling the inverter output currents. The typical method to obtain full information of output phase currents is to measure them

directly using three current sensors (or at least two). An alternative to DHR is DDHR which was presented recently in [20]. In DDHR, the phase currents are reconstructed using measured dc-link current and knowledge of the switching states. For the drive shown in Fig. 2, the switching states and correlation to is detailed in Table II. It is noted that in state 0 or 7, either all of is the upper switches or lower switches are turned on, thus always 0 and doesn’t represent any phase current. This must be avoided as described in the following part of this paper. In all represents one of the phase currents. other states, and the three phase Based upon the relationship between currents, DDHR was developed for accurate reconstruction and control of the phase currents using a single dc link current sensor [20]. A block diagram of DDHR is shown in Fig. 3. Within the represents reconstructed phase curblock diagram represents commanded phase currents, and rents, is the measured dc-link current. The vector repis the switching state resents the present switching state, one obtains by comparing commanded current to reconstructed is the actual switching comphase currents, and mand to be sent to the gate drive at the next clock cycle. The block diagram in Fig. 3 shows that DDHR is composed of two parts. The lower portion is the current reconstruction where the phase current is determined as close as possible from 1) the dc link current and 2) switching states. The upper part is the switching state generator (SSG) that establishes the next




Fig. 4. Block diagram of current reconstruction.

switching state based upon 1) commanded phase currents, 2) reconstructed phase currents, and 3) present switching states. A block diagram of the current reconstruction is shown in Fig. 4. Different from the method presented in [17], this line current reconstruction scheme is based upon real-time digital techniques. The requirement of analog circuit is eliminated. It also incorporates a digital filtering technique to obtain more accurate current reconstruction when the dc-link current does not have the instantaneous transitions one ideally expects from switching (due to loop inductance, noise, etc.) at each switching instant. The functions of the SSG are to: 1) generate the next switching state and 2) ensure that the dc-link current always contains sufficient information to reconstruct three phase currents. In Fig. 3, the first function is realized by generating 1 through the hysteresis comparison of commanded and reconstructed current. The second function is realized by and preconsidering both the present switching state 1 before generating ferred hysteresis switching state 1 . the next switching state Since the reconstructed current is used as feedback in the current loop, it is possible that the inverter will maintain operation in a single switching state or continually switch between states that update the same phase current. States 1&6, 2&5, and 3&4 are examples of pairs of states that, if the inverter keeps switching between them, only one phase current is updated. If this so-called sticking of state pairs occurs, the other two currents, with the error accumulating, will become farther apart from their actual values, causing the control to be unstable [19]. In DDHR, if the inverter becomes stuck in a state pair for a respectively long time (several loop periods), a control strategy is applied to force the next switching state to choose another state that will update a different current. A detailed state selection table for the observer based DDHR method is provided in Table III. Details of state selection are presented in [20]. Failure of two phase current sensors represents a relatively extreme condition. In the event of a single phase current sensor, the current reconstruction remains similar to that shown in Fig. 4; however, the healthy sensor is used to update the respective phase and therefore reconstruction is only performed on two phases. In the state generation, the forbidding of the zero/seven switching state remains necessary. However, the sticking of pairs becomes much less of an issue and therefore Table III can be greatly simplified. As an example, assuming

the phase- current sensor is healthy and one is in switching state 2/5, the dc current will represent the phase- current. Therefore, the state will not become stuck in 2/5. By similar logic one will also not become stuck in 3/4. The only remaining possible sticking states are 1/6, since when one is in 1/6 the dc current will represent no new phase information. Herein the operating mode in which a healthy phase current sensor is used within DDHR is referred to as DDHRPS. One drawback of DDHR (and to a lesser extent DDHRPS) is that the current waveform has considerable high frequency components. To illustrate this, the currents obtained from simulating an induction motor drive (parameters described in Section IV) under DHR, DDHR, DDHRPS are shown in Fig. 5. The FFT response is scaled to the fundamental component of the respective waveform. Comparing responses it is clear that DDHR has the most significant frequency harmonic content in the switching frequency range, followed by DDHRPS, and DHR. The high frequency component in the current waveform will be translated to high frequency torque ripple. Although in some applications high-frequency torque ripple can be undesirable, in most cases the motor acts as a low pass filter to high frequency torque ripple, so, the effect of high frequency torque ripple on the speed control is not a significant performance issue. III. SENSOR FAULT DIAGNOSIS AND TOLERANCE SCHEME System sensor fault-tolerance can be realized through a flexible controller structure. The controller monitors for sensor faults and recoveries and adopts the best control strategy to obtain the highest drive performance out of the remaining sensors. Thus, the motor controller consists of fault diagnosis and controller reconfiguration parts. A. Sensor Fault Diagnosis Scheme Sensor fault diagnosis (FD) is a key issue for fault-tolerance. There is a tradeoff between the amount of computation that a FD scheme requires and the amount of information that the FD scheme provides. An accurate FD may lead to a time delay that results in large transients, while a small time delay might result in a false alarm. Due to the complexity of sensor fault behavior,



Fig. 6. Schematic for the phase current sensor.

Fig. 7. FD circuit for the phase current sensor.

to the current amplitude. The phase current sensor FD circuit is shown in Fig. 7. When an open circuit disconnection occurs, the output signal is set to ground. This signal is input to the DSP and is sampled as zero current. Meanwhile, the reconstructed phase currents obtained using information from the dc-link current provides reference currents that are very close to the actual phase currents. Comparison of the measured current and the reference current provides a relatively straightforward method for FD. For example, the phase- current sensor is detected to have a disconnection fault only if 1) the phase current sensor is previously healthy (no fault), 2) the measured phase current is zero, and 3) the reconstructed current is nonzero. Otherwise, when a measured phase current is zero and the reconstructed current is zero, it means that the phase current sensor is healthy, and that the current was commanded to be zero. A phase current sensor is detected to be recovered from a disconnection fault only if 1) the phase current sensor is previously faulted and 2) the measured phase current is nonzero. The logic of the FD scheme is shown in the following: Fig. 5. Comparison of line current waveform under DHR, DDHR, and DDHRPS (simulation).

in this paper, only intermittent and continuous disconnection faults are considered. Since the rotor position and phase currents are estimated using information from the dc-link, for the fault diagnosis of position and phase current sensors the estimated values can serve as reference signals. These reference signals greatly shorten the time required for FD to provide reasonably accurate information on sensor faults and recoveries. The phase current sensor FD is provided here as an example. The phase current sensors used in the drive system are CLN-50 close loop Hall effect current sensors as shown in Fig. 6. The output of CLN-50 acts as a current source proportional to the actual phase current. The current output of CLN-50 flows through to obtain a voltage output proportional the resistance

IF phase current sensor is healthy THEN N IF


for N continuous cycles


sensor is fault ELSE IF phase current sensor is fault THEN

IF for continuous cycles THEN sensor is recovered (5)



where is the -phase measured phase current, is the -phase reconstructed phase current and is a small positive or is less than , value. When the absolute value of or is deemed to be equal to zero, otherwise, it is nonzero. is the number of cycles in which the criteria— and —happens continuously. must be set large enough to bypass any fault mistrigger due to noise, sampling error and reconstruction error, and yet small enough to ensure a fast FD and to avoid a large transient. The FD time is a function of the switching frequency. Specifically, the time can be expressed


(6) is the time required for fault diagnosis, and is the where insulated gate bipolar transistor (IGBT) switching frequency. It is assumed the DSP operates at a rate of at least two times the switching frequency. In the prototype system, the switching fre10. quency is 20 kHz. The time for FD is 0.25 ms when This FD time has proven to be fast enough to eliminate significant transients, and accurate enough to not cause mistrigger. The FD circuit in Fig. 7 and the FD criteria in statement (5) are also applied to detect the fault and recovery of the phase current sensor under short circuit, in which the currents measured are at a value of zero. A similar FD circuit and criteria is used for the position sensor. The FD circuit in Fig. 7 could also be applied to the dc-link current sensors. However, as opposed to the phase current sensors, the dc-link current does not have a reference current for comparison. Therefore, the FD time for the dc-link current sensor is longer than that of the phase current sensors. Further analysis on how to use the reference signal to help detect other sensor faults such as noise, drift, and offset is the subject of ongoing research. B. Sensor Fault Tolerance Scheme Table IV lists six possible operation modes relative to the sensors available in the system. The first mode is the normal operation mode, wherein the phase current sensors and the position sensor are available. In this mode, the controller selects to operate in indirect vector control. In the second mode, the position sensor is faulted and the phase current sensors and the dc-link voltage sensor are available. In this mode, the MRAS sensorless vector control is adopted as a position sensor fault-tolerance scheme to estimate rotor speed as shown in Fig. 2. The third mode shows the phase current sensors are faulted and the position and the dc-link current sensor are available. In Mode 3, DDHR is adopted as a phase current sensor faulttolerance scheme to reconstruct and control the phase currents. In Mode 4, both position sensor and phase current sensors are faulted and only the dc-link current and voltage sensors are available. The control strategy in Mode 4 is a combination of MRAS and DDHR. Since the phase currents can be reasonably well reconstructed and controlled using knowledge of the dc-link current and switching state as described above, the reconstructed currents are fed into the MRAS speed identification to estimate the rotor speed. Modes 3 and 4 shown in Table IV

represent a worst case scenario in terms of loss of both phase current sensors. In the event that a single phase current sensor is lost, DDHRPS can be used in Modes 3 and 4 in lieu of DDHR. Fig. 8 shows the block diagram of the MRAS speed identification scheme combined with DDHR scheme. Fig. 9 shows the block diagram of Mode 4 with the MRAS speed identifier and DDHR current regulation. In Fig. 9, the slip is calculated and used with the estimated speed to obtain the flux angle. An alternative to obtain the flux angle is directly from the MRAS. However, to make the controller reconfiguration easier, it is convenient to have the structure of Mode 4 as similar as possible to other operation modes. Thus, the calculation of flux angle is selected to be similar to other operation modes. Through Mode 1–4, field oriented control is maintained. However, there is some performance deterioration. Mode 1 has accurate position and phase current information and has the best performance. In Mode 2, the rotor speed is estimated, but the estimation has relatively poor performance at low speeds. In Mode 3, the phase current is reconstructed and controlled through the dc-link current sensor, but some high frequency ripple is induced into the phase current and hence the torque. Mode 4 adopts both the MRAS and DDHR and thus, combines the disadvantages of both methods. Operation in Modes 5 and Mode 6 are detailed in [12] and therefore will not be presented here in detail. In Mode 5, only



Fig. 8. Block diagram of MRAS combined with DDHR. Fig. 10. Block diagram of the drive system sensor fault-tolerance control scheme.

Fig. 11. Induction motor and load drive system. Fig. 9. Block diagram of the motor controller in Mode 4.

IV. SIMULATION AND EXPERIMENTAL RESULTS the phase current sensors are available and therefore scalar current magnitude control is utilized. As a result, the torque control has a deteriorating dynamic performance. Mode 6 is considered the last step for the sensor fault-tolerance, where volts/hertz is selected as the control scheme. A desired voltage is applied to the drive based upon the desired rotor angular velocity, and thus, knowledge of dc-link voltage is necessary for the volts/hertz control. A diagram for the drive system sensor fault-tolerance is shown in Fig. 10. In Fig. 10, the symbols in the parentheses, , , , ) represent the sensors that are available in ( the respective operating mode. The symbol underlined shows that they are used to provide feedback in the controller. Using this fault tolerance strategy, the drive system maintains vector control even if both the position sensor and the phase current sensors are lost and only the dc link current sensor and voltage sensor are available.

Simulation and experiments have been performed to verify the fault-tolerance scheme. The simulation and experiments are focused upon the transitions from Mode 1 through Mode 4. Both steady state and transients are tested. However, steady-state operation within Mode 1 and 2 are mature control schemes. The steady state performance of operation Mode 3 is detailed in [20]. Therefore, in this paper, the discussion of steady state operation of Modes 1, 2, and 3 are not detailed. Steady state operation of Mode 4 and transients between Modes 1 and 2, 1 and 3, 2 and 4, and 3 and 4 are considered. The hardware used for the experiments is shown in Fig. 11. It consists of an inverter, 3.7-kW induction motor, a controller implemented within a TMS320F2812 processor, and a dynamometer. The dynamometer has been configured to provide speed and torque measurement and the system is operating at a 20-kHz PWM frequency. The parameters of the motor used in the experiments and simulations are detailed in Table V.




Fig. 13. Startup speed and torque waveform under Mode 1 (traditional FOC)simulation.

Fig. 12. Startup speed and torque waveform under Mode 4 (simulation). Fig. 14. Comparison of measured and estimated rotor speed in Mode 4 (experiment).

For initial testing, simulation and experiment under operation Mode 4 have been performed to establish the startup response as well as the steady state performance of the drive for a commanded speed of 800 rpm. Simulation results are shown in Figs. 12 and 13. Fig. 12 shows the startup speed and torque waveform under operation Mode 4. Fig. 13 shows the startup speed and torque waveform under Mode 1 (where both position and phase current sensors are healthy). Fig. 14 shows experimental results for the estimated and measured rotor speed in Mode 4. Fig. 15 shows the experimental results for the phasecurrents operating in Modes 1 and 4. Comparing responses, it can be seen that both operational mode 4 and traditional FOC (mode 1) yield nearly the same response. One difference between the respective responses can be seen in comparing the simulated torque waveforms in steady state. Specifically, it can be seen that there is a higher level of torque ripple in the MSFOC. This is due to the fact that the current waveform of DDHR has considerable high frequency components-shown experimentally in Fig. 15. Fig. 12 shows that when position and phase current sensors fail, some performance of the system is sacrificed. Although in some applications high-frequency torque ripple can be undesirable (such as navy ship propulsion systems where detection is an issue), the motor often acts as a low pass filter to high frequency torque

Fig. 15. Comparison of phase current waveforms under Mode 1 and Mode 4 (experiment).

ripple. Therefore, the effect of high frequency torque ripple on the speed control is not observable as seen in Figs. 12 and 14.


Fig. 16. Drive system performance when position sensor is faulted (experiment).

A second set of experiments was performed to verify that the transient related to the fault diagnosis and the controller reconfiguration is relatively minor. The speed response, torque, and and - and -axes current were measured as sensors were faulted or recovered. To be more specific, the drive system was operated under speed control with a 3-Nm load torque. The commanded speed to the drive was 800 rpm. In addition to the sensors used to control the drive, a set of sensors was used to monitor the drive system behavior under faults. Using these “monitor sensors,” the speed and - and -axis currents under fault conditions were recorded as faults occurred. They were input to a model of the machine to calculate the behavior of the electromagnetic torque under the transient conditions. Experimental results are shown in Figs. 16–19. Figs. 16 and 17 show the speed transients, electromagnetic torque, and current when the position sensor in the drive system is faulted and consequently recovered. From these plots it can be observed that there is relatively minor change in rotor speed during both fault and recovery. The small difference in speed that does occur is the result of changes in the -axis current that results as the transition between measured and estimated speed occurs. Interestingly, there is almost no effect on the -axis current during the transitions. It can be observed that it takes roughly 0.25 ms for the fault detection scheme to detect the sensor fault or recoveries after which the controller switches


Fig. 17. Drive system performance when the position sensor is recovered (experiment).

from operation Mode 1 to Mode 2 under position sensor failure or from operation Mode 2 to Mode 1 under position sensor recovery. Figs. 18 and 19 show the speed, torque, and current transients when the phase current sensors are faulted or recovered. Therein, the controllers are reconfigured from operation Mode 1 to Mode 3 when phase current sensors are faulted or from Mode 3 to Mode 1 when phase current sensors are recovered. As shown in Figs. 18 and 19, there exists a significant transient in the electromagnetic torque under both conditions. This is primarily caused by the misalignment of the stator field with respect to the rotor airgap flux. In fact, during transient the loss of measured stator current means that the calculated -axis current differs from that of the actual field oriented system during this time. Compared to the case in which the speed sensor is lost, here the speed drop change is more significant and the recovery time is longer. Also of interest is that once in Mode 3, currents and torque is more the high frequency ripple on the pronounced, due to the higher harmonics introduced by DDHR. Further experiments show that when the controller switches between operation Modes 2 and 4, the speed transient is very similar to that of the controller switching between operation Modes 1 and 3. Specifically, when the controller switches between operation Modes 3 and 4, the speed transient is nearly identical to that of the controller switching between operation Modes 1 and 2.


Fig. 18. Drive system behavior when phase current sensors are faulted (experiment).

The fault diagnosis and tolerance scheme has been tested experimentally for a speed range from (200–1800 rpm) with similar results to those shown in Figs. 16–19. V. CONCLUSION Induction drive systems in automotive applications require fault-tolerance as well as wide-bandwidth performance. A sensor fault diagnosis and reconfiguration scheme for an induction drive system has been developed wherein intermittent or continuous disconnection types of sensor faults are considered. The scheme can diagnose sensor faults and recoveries, and based upon the fault status, the controller selects the control algorithm to maintain the best performance using the remaining sensors. The fault diagnosis and fault-tolerance schemes combine the profits of MRAS sensorless vector control and DDHR current regulation to ensure the system operates under FOD even if all the phase current sensors and position sensors fail. The complete control structure has been implemented in a DSP-controller system. Simulation and experimental results are presented to verify the effectiveness of this scheme. REFERENCES [1] D. Kastha and B. Bose, “Investigation of fault modes of voltage-fed inverter system for induction motor drive,” IEEE Trans. Ind. Appl., vol. 30, no. 4, pp. 1028–1038, Jul./Aug. 1994. [2] B. C. Mecrow, A. G. Jack, J. A. Haylock, and J. Coles, “Fault-tolerant permanent magnet machine drives,” Proc. Inst. Elect. Eng., vol. 143, no. 6, pp. 437–442, Nov. 1996.


Fig. 19. Drive system behavior when current sensors are recovered (experiment).

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[14] S. Tamai et al., “Speed sensorless vector control of induction motor with model reference adaptive system,” in Proc. IEEE-IAS Annu. Meeting, 1987, pp. 189–195. [15] T. A. Lipo, “Recent progress in the development in solid-state ac motor drives,” IEEE Trans. Power Electron., vol. 3, no. 2, pp. 105–117, Apr. 1988. [16] R. D. Lorenz and D. M. Divan, “Dynamic analysis and experimental evaluation of delta modulators for field-oriented ac machine current regulators,” IEEE Trans. Ind. Appl., vol. 26, no. 2, pp. 296–301, Mar./ Apr. 1990. [17] T. Green and B. Williams, “Derivation of motor line current waveform from the DC-link current of inverter,” Proc. Inst. Elect. Eng. B, vol. 136, no. 4, pp. 196–204, Jul. 1989. [18] F. Blaabjerg and J. K. Pedersen, “Single current sensor technique in the DC-link of three-phase PWM-VS inverters. A review and the ultimate solution,” IEEE Trans. Ind. Appl., vol. 33, no. 5, pp. 1241–1253, Sep./ Oct. 1997. [19] T. G. Habetler and D. M. Divan, “Control strategies for direct torque control using discrete pulse modulation,” IEEE Trans. Ind. Appl., vol. 27, no. 5, pp. 893–901, Sep./Oct. 1991. [20] H. Wang, S. Pekarek, and B. Fahimi, “A digital implementation of deltahysteresis current regulation,” in Proc. IEEE Workshop Comp. Power Electron., Aug. 15–18, 2004, pp. 89–95. [21] J. Holtz, “Sensorless control of induction motors-performance and limitations,” in Proc. IEEE ISIE’00, Dec. 2000, vol. 1, pp. PL12–PL20. [22] J. Hu and B. Wu, “New integration algorithms for estimating motor flux over a wide speed range,” IEEE Trans. Power Electron., vol. 13, no. 5, pp. 969–977, Sep. 1998. [23] J. Holtz and J. Quan, “Drift and parameter compensated flux estimator for persistent zero stator frequency operation of sensorless controlled induction motors,” in Proc. 37th IAS Annu. Meeting, Oct. 2002, vol. 3, pp. 1687–1694. [24] J. Holtz and J. Quan, “Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification,” IEEE Trans. Ind. Appl., vol. 38, no. 4, pp. 1087–1095, Jul. 2002. Hainan Wang received the M.S. degree in electrical engineering from Tsinghua University, Beijing, China, in 2001 and the Ph.D. degree in electrical engineering from the University of Missouri, Rolla, in 2004. He is currently a Senior Electrical Engineer with Pioneer Magnetics, Los Angeles, CA. His research interests include fault tolerance of motor controller, digital power supply design, modeling, and dynamic analysis of power electronics systems.

Steve Pekarek (M’05) received the Ph.D. degree in electrical engineering from Purdue University, West Lafayette, IN, in 1996. From 1997 to 2004, he was an Assistant (Associate) Professor of electrical and computer engineering with the University of Missouri, Rolla. He is presently an Associate Professor of electrical and computer engineering at Purdue University and is the co-Director of the Energy Systems Analysis Consortium. As a faculty member he has been the principal investigator on a number of successful research programs including projects for the Navy, Airforce, Ford Motor Co., Motorola, and Delphi Automotive Systems. The primary focus of these investigations has been the analysis and design of power electronic based architectures for finite inertia power and propulsion systems. Dr. Pekarek is a member of the IEEE Power Engineering Society, the Society of Automotive Engineers, and the IEEE Power Electronics Society.

Babak Fahimi (S’96–M’00–SM’02) received the Ph.D. degree in electrical engineering from Texas A&M University, College Station, in 1999. Currently, he is with the Department of Electrical Engineering, University of Texas, Arlington, as an Assistant Professor. His areas of interest include microscopic analysis of electromechanical energy conversion, digital control of adjustable speed motor drives, and design and development of power electronic converters.

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