Proposition of an Offline Learning Current Modulation for Torque-Ripple Reduction in Switched Reluctance Motors: Design and Experimental Evaluation

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1 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE Proposition of an Offline Learning Current Modulation for Torque-Ripple Reduction in Switched Reluctance Motors: Design and Experimental Evaluation Luis Oscar de Araujo Porto Henriques, Member, IEEE, P. J. Costa Branco, Member, IEEE, Luís Guilherme Barbosa Rolim, and Walter Issamu Suemitsu, Member, IEEE Abstract A new offline current modulation using a neuro-fuzzy compensation scheme for torque-ripple reduction in switched reluctance motors is presented. The main advantage of the proposed technique is that the torque signal is unnecessary. The compensating signal is learned prior to normal operation in a self-commissioning run, capturing the necessary current shape to reduce the torque ripple. Simulation results verify first the effects of speed and then load changes on the compensator performance. Implementation of the proposed technique in a laboratory prototype shows the feasibility and accuracy of the respective offline scheme. Index Terms Automatic learning, fuzzy neural networks, intelligent control, switched reluctance drives, torque-ripple minimization. I. INTRODUCTION TORQUE-RIPPLE reduction in switched reluctance motors (SRM) has become a major research theme for this machine today. In servo control applications or when smooth control is required at low speeds, reduction of the torque ripple becomes the main issue in an acceptable control strategy. In this case, even using a fuzzy proportional-plus-integral (PI)-like control such as the one described in [1], the results are not satisfactory because the controller s output signal, which is used as a reference signal for the current control in the power converter, causes sustained torque oscillations in steady state. Furthermore, torque ripple alters with the speed of the SRM and with the magnitude of the load applied to it. The first approach for torque-ripple reduction in SRMs using an offline learning technique is proposed in [2]. A step-forward offline approach is presented in [3] and [4], which also uses soft-computing techniques to learn the best function to reduce the ripple. Recently, more sophisticated learning control algorithms were proposed [5] [7] that enhanced online approaches adapting the controller to changes in the motor s characteristics. However, these approaches need to be improved concerning two aspects. Instead of starting from a zero knowledge (all rules at a zero value), the fuzzy system responsible for the ripple reduction can be initialized with rules obtained from a previous simulation study with the SRM. In this paper, a new offline learning strategy, which has used a simulation model of the SRM to acquire the initial knowledge rule base, is proposed. The use of the machine torque, measured or estimated, to implement the torque-ripple reduction techniques online, decreases their robustness and limits the application of the online control algorithm mainly due to costs. Hence, a compensation scheme that allows its online use without measurement or estimation of the torque signal needs to be researched. Based on these two aspects, we have decided to take a step backward and, first, redesign an offline approach that will allow us to design a compensation scheme using a learning scheme that does not use a torque signal. In the technique proposed in this paper, the compensating signal is added to the output of a PI controller, in a current-regulated speed control loop, which adjusts automatically the machine currents to reduce the torque ripple of the motor. The simulated study and the implementation of the proposed technique are presented in this paper. The experimental results achieved show how the motor phase currents are shaped to reduce the torque ripple, also considering different load values. II. SR DRIVE MODELING The electromagnetic equation of each SRM phase is given by Manuscript received February 23, 2001; revised October 24, Abstract published on the Internet March 7, L. O. A. P. Henriques, L. Rolim and W. I. Suemitsu are with the Universidade Federal do Rio de Janeiro/COPPE-Elétrica, CEP Rio de Janeiro, Brazil ( walter@dee.ufrj.br). P. J. Costa Branco is with the Mechatronics Laboratory, Instituto Superior Técnico, Lisbon , Portugal ( pbranco@alfa.ist.utl.pt). Publisher Item Identifier S (02)04916-X. with each phase torque originated by the respective magnetic co-energy variation as (1) (2) /02$ IEEE

2 666 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE 2002 Fig. 1. (a) Inductance profile L(; i). (b) Magnetic flux 9(; i). (c) Phase torque T (; i). The mechanic equation of the SRM using the phase torque expression in (2) results in where inertia; rotor speed; phase voltage; stator phase resistance; phase current; flux linkage; phase torque; load torque; rotor position; magnetic co-energy. Equation (1) consists of the instantaneous voltage across the terminal of each phase of the machine winding, which is divided into a voltage drop at stator resistance plus the voltage induced (3) in the winding by flux linkage variation. The SRM has a variation of the magnetic co-energy due to its doubly salient structure and, therefore, produces the reluctance torque given by (2). The linkage flux, and thus each phase inductance, is a function of position and current due to SRM mechanical structure and the nonlinearity of the SRM magnetic characteristic. In [8], the 6/4 SR machine used in this work was modeled by finite-element analysis (FEM) providing us with its magnetic data. Each phase inductance profile and respective flux linkage are shown in Fig. 1(a) and (b), respectively. The phase torque is computed by (2) using the well-known definition of co-energy given by resulting in the phase torque profile shown in Fig. 1(c). To energize the SRM, shown in Fig. 2(b), a -bridge asymmetric-type converter has been used, as seen in Fig. 2(a). The converter uses insulated gate bipolar transistors (IGBTs) with (4)

3 HENRIQUES et al.: PROPOSITION OF AN OFFLINE LEARNING CURRENT MODULATION 667 Fig. 2. (a) H-bridge power converter. (b) 6/4 SR machine. freewheeling diodes, and the continuous voltage is obtained from a diode rectifier. A hysteresis current controller was used, with a microcomputer establishing the energizing and deenergizing angles, and the reference current signal. Sometimes, for a high-speed motor operation, the induced voltage electromotive force (EMF) can have the same order of magnitude of the voltage. In this case, a voltage reserve becomes necessary to maintain the voltage supply high enough to guarantee the hysteresis current control. When this is not possible, the current control scheme has to be changed to single-pulse operation. Using the above prototype, tests [9] were made to verify the respective SR drive model developed. These tests were performed with the prototype operating in open-loop mode and for a set of operating conditions. Fig. 3(a) shows the phase current simulated for a reference of 2 A, nominal dc voltage V, and with and. In Fig. 3(b), the same conditions have been used in the prototype, showing the phase current measured during the machine operation with a close correlation with the simulated currents. For comparison of the machine steady-state operation obtained with the model and with the experimental prototype, two tests were performed. In these tests, we considered two reference current values, A and A, and variation of the angle from 40 to 67, maintaining the value constant. Fig. 3(c) and (d) shows the measured and simulated motor speeds obtained for 1.5 and 2.5 A, respectively. From the results achieved, it is shown that the steady-state responses of the model match well the measured data. III. PROPOSED TECHNIQUE A. Compensation Scheme The SR drive has been designed to operate in a speed control mode. Fig. 4 shows, in a block diagram, the proposed compensation scheme. The basic idea for the proposed ripple compensation scheme is described in Fig. 5. The output signal produced by the compensator,, is added to the PI controller s output signal, which, ideally, should be constant in steady state but producing significant ripple, as shown in Fig. 5(a). The resulting signal after the addition is used as a compensated current signal for the SR drive, as shown in Fig. 5(b). The compensating signal should then be adjusted in order to produce a ripple-free output torque. As shown in Fig. 4, this signal can be a function of rotor position, motor speed, torque load value, and the phase current (5) In fact, (5) is a function that possesses high mathematical complexity and, therefore, the production of this signal is quite complicated. As a first solution to decrease the function complexity, the number of variables was reduced to only two: rotor position and the current, which was replaced by the PI current signal also correlated with the magnitude of the load torque. Hence, (5) becomes The compensating function should then be learned in order to produce the necessary adjustments to the PI current signal, thus reducing the torque ripple. (6)

4 668 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE 2002 Fig. 3. Steady-state performance. (a) Simulated and (b) experimental phase currents. Measured and simulated motor speeds for (c) I = 1:5 A and (d)i = 2:5 A. Fig. 4. Block diagram of the SR drive system with the compensating signal. B. Offline Learning of Compensating Function Fig. 5(c) shows the proposed compensation scheme. The neuro-fuzzy compensation block uses the rotor position and the PI current signal as inputs, with its output being the compensating current increment. The first step of compensation corresponds to the offline training of the neuro-fuzzy compensator. This is adjusted iteratively through the learning algorithm, with the torque ripple signal used as the training error information.

5 HENRIQUES et al.: PROPOSITION OF AN OFFLINE LEARNING CURRENT MODULATION 669 (a) (b) (c) Fig. 5. Basic idea of proposed torque-ripple compensation technique. (a) Torque ripple produced by constant current reference. (b) Ripple-free torque produced by compensated phase current. (c) Block diagram of the proposed neuro-fuzzy torque-ripple compensator. 1) Neuro-Fuzzy System Structure: The adaptive-networks based fuzzy inference system (ANFIS) [12] has been used to implement the compensator. Fig. 6(a) shows its network structure, which maps the inputs by the membership functions and their associated parameters, and therefore through the output membership functions and corresponding associated parameters. These will be the synaptic weights and bias, being associated to the membership functions adjusted during the learning process. The computational work for the parameters acquisition (and their adjustments) is helped by the gradient-descendent technique, which shows how much the error decreases. When the gradient is obtained, any optimization routine can be applied to adjust the parameters and, therefore, decrease the error. The ANFIS neuro-fuzzy system operation can be summarized in two steps. a) The set of membership functions has to be chosen, i.e., their number and corresponding shape. b) The input output training data are used by the ANFIS system. Initially, this system makes a clustering study of the data to identify natural groups of data, to obtain a concise and significant representation of the system s behavior. As we do not know how many clusters exist (the number of rules composing the neuro-fuzzy compensator), another technique must be used, the subtractive clustering [13], which quickly estimates the number of clusters. 2) Learning Scheme: A second part consists in the iterative training of the neuro-fuzzy compensator. The presence of this iteration comes from the capability to simulate the system and, after a predefined simulation time, to obtain the simulation results and use them in the learning process. Training data were obtained from simulations of steady-state operation of the complete SR drive system. At each training iteration, the dc component was removed from the torque signal so that only the ripple remains. This torque ripple data is then tabulated against the mean value of the PI current and against the rotor angular position. This data set is then passed to the training algorithm, so that the torque ripple is interpreted by the compensator as error information for each current angle pair. The compensator output is then readjusted to reduce the error (which is, in fact, the torque ripple), and this process is repeated until some minimum value torque ripple has been reached. Fig. 6(b) shows the process for training the neuro-fuzzy compensator. The output of the training block comprehends all main variables from the system, and only is used in the simulated system to train it again. The compensating current [shown as the dotted line in Fig. 6(b)] is also produced and used in the system for the next training iteration. The halting criterion to stop is, in this case, the maximum number of iterations set a priori. When the iteration counter reaches the learning process stops. Note that the training time will depend on the microcomputer speed and mainly on the set maximum number of iterations. The choice of stopping criteria is very important for the stability of the method, since the converter may not be able to produce the required compensated currents at any speed or load. In this case, persisting on training may lead to output windup in the compensator. The main reason for this is that, after training, the compensator can require current magnitudes that could not be reached by the converter. Therefore, a compromise between the converter capabilities and the currents required by the compensator is needed. The optimization of the neuro-fuzzy system was performed by a hybrid technique that uses the backpropagation and the mean-squared error method. The rule set was initially generated by the grid partition technique. After training, the compensator signal can be generated and added to the reference current from the PI controller. By varying the PI current in discrete values and the rotor position, the compensating relation was extracted and is shown in Fig. 6(c). In this figure, the compensation values below 1 A are smaller then the values found with currents near 3 A. This happens because, for the low speed produced with this current, there is no need for high compensation current values.

6 670 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE 2002 Fig. 6. (a) ANFIS network structure. (b) Training program flowchart. (c) Compensating function. IV. SIMULATION RESULTS Simulation results with the proposed technique are presented for different situations. Each one shows current in phase A, the total machine torque, and its corresponding harmonic spectrum. A. Initial Tests For comparison purposes, the SR drive system has been simulated without compensation, at full-load torque (approximately 4-N m mean value), and a motor speed of 500 r/min. The torque signal is plotted in Fig. 8(c) and its harmonic components are

7 HENRIQUES et al.: PROPOSITION OF AN OFFLINE LEARNING CURRENT MODULATION 671 (a) (a) (b) (b) (c) (c) (d) (d) Fig. 7. Phase current for 1800 r/min: (a) without and (b) with compensation. Phase current for 500 r/min: (c) without and (d) with compensation. plotted in Fig. 9(c). Since the machine has a 6/4 structure, the converter produces 12 current pulses per rotor turn. Therefore, torque pulsation occurs at a frequency 12 times higher than the frequency of rotation. This is the reason why the harmonic spectrum exhibits nonzero components only for orders multiple of 12. The magnitudes of the harmonics are expressed as percentage of the mean value. It should be noticed in Fig. 9(c) that the first nonzero harmonic (12th) exhibits a quite high magnitude (approximately 13%). However, after ten training iterations of the neuro-fuzzy compensator, the harmonic content of the output torque becomes significantly lower, as shown in Figs. 8(d) and 9(d). It can be seen that the total harmonic content is now very low, and the 12th harmonic becomes lower than 0.5% of the mean torque. Fig. 8. Total torque for 1800 rpm: (a) without and (b) with compensation. Total torque for 500 rpm: (c) without and (d) with compensation. After ten training iterations, the compensated current reference produces phase current pulses like those shown in Fig. 7(d). As expected, the current values are higher at the beginning and at the end of the current pulse. This pulse shape is consistent with the torque characteristics of the SR motor shown in Fig. 1(c), which produces less torque at the beginning of pole overlapping and just before the aligned position. B. Generalization Tests We show the compensator action for two different motor speeds. Results are presented as current in phase A, total torque, and corresponding frequency spectrum. 1) Current in Phase A: Fig. 7(a) and (b) shows the current signal in phase A before and after the addition of the compensating signal for the nominal speed operation

8 672 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE 2002 (d) Fig. 9. Frequency spectrum of the total torque for 1800 r/min: (a) without and (b) with compensation. Frequency spectrum of the total torque for 500 r/min: (c) without and (d) with compensation. (a) (b) (c) (1800 r/min), respectively. In a similar way, Fig. 7(c) and (d) shows the current for a speed of 500 r/min. 2) Total torque: The total torque before and after compensation is shown in Fig. 8. The variance signal of torque was used as a quantitative measurement of torque ripple as indicated in each result. The results clearly show the effects of the compensating signal, mainly Fig. 8(d) where it can be seen that this technique is very efficient to low speeds. 3) Frequency Spectrum: Fig. 9 shows the frequency spectrum of the total torque for both motor speeds before [Fig. 9(a) and (c)] and after [Fig. 9(b) and (d)] the compensation. As the converter has 12 pulses by rotation, it is clear that the predominant harmonic is of the 12th order and its multiples, and is then reduced after the compensation process. V. TEST DRIVE AND EXPERIMENTAL RESULTS A switched reluctance drive prototype was built for our tests. It consisted of the 6/4 SR motor, 750 W, with nominal speed equal to 1800 r/min shown in Fig. 2(a), and the H-bridge power converter shown in Fig. 2(b). The converter used IGBTs with freewheeling diodes, and the continuous voltage was obtained from a diode rectifier. A hysteresis current controller was used with a microcomputer establishing the energizing and deenergizing angles, and the reference current. Note that the signal, used by the hysteresis controller with an hysteresis band of 0.1 A, is the sum of the compensation signal and the current produced by the PI controller. A. Open-Loop Compensation The first experimental test was done with the SR drive operating in the open-loop mode, without the PI speed controller, for a constant reference current of 2 A and without external load applied. The reason for this test was to verify the pulse current modulation and the consequent ripple reduction without influence of the PI controller dynamics on the speed control loop. Two situations were taken into consideration: one without compensation and other when the neuro-fuzzy compensator is inserted into the SR drive system. For this test, Fig. 10(a) shows the phase current signal when there is no compensation, and Fig. 10(b) shows the current when the neuro-fuzzy compensator is inserted. The current shape is modified following the curve previously learned and stored in the compensating function in Fig. 6(c) for A. The current correction follows the shape of the compensation curve in Fig. 10(c) between 45 90, which are the necessary increments to be added in the reference current of 2 A. Note that all current pulse shapes follow the left side of the compensation curve since the motor during the tests had its speed reversed. Fig. 10(d) shows the frequency spectrum of the motor speed signal before the compensator has been inserted, and Fig. 10(e) the spectrum after the compensation. B. Closed-Loop Compensation In this second test, the PI speed controller was inserted in the SR drive system. The essay was performed for a reference motor speed of r/min, equal to that reached in the previous test, and since no external load was applied, the current reference to be achieved was also about 2 A, as before. Again, two situations have to be observed: before and after the neurofuzzy compensation. Fig. 11(a) shows the current signal when there is no compensation and Fig. 11(b) shows the current signal when the compensator was inserted. C. Closed-Loop Compensation External Load This test was made with an external load applied to the SR motor by a PM synchronous generator, which was connected to an external resistance of 500 W through a three-phase diode bridge. The PM generator has a high inertia constant. Therefore, there was not a significant torque ripple in the SR motor provoked by the PM generator. Fig. 11(c) and (d) shows the phase current before and after compensation, respectively. When the compensator was inserted, the current shape was changed to follow the learned compensating curve shown in Fig. 11(e),

9 HENRIQUES et al.: PROPOSITION OF AN OFFLINE LEARNING CURRENT MODULATION 673 Fig. 10. I Phase current (a) without and (b) with compensation (vertical: 1 A/div and horizontal: 50 ms/div =40 mechanical degrees). (c) Compensation signal for = 2 A. Frequency spectrum of the motor speed (d) before and (e) after compensation. which shows the necessary increment to be added to the new reference current of about 3 A. A second test was done with a double load applied. Fig. 12(a) and (b) shows the current pulses before and after

10 674 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE 2002 Fig. 11. PI closed-loop (a) without and (b) with compensation. PI controller + external load for (c) no compensation and (d) compensation (vertical: 1 A/div and horizontal: 50 ms/div =40 mechanical degrees). (e) Compensation signal for I =3A. compensation, respectively. The current shape was changed to follow the compensating curve in Fig. 12(c). Fig. 12 shows the frequency spectrum of the motor speed signal before [Fig. 12(d)] and after [Fig. 12(e)] the compensator was inserted, revealing again the torque-ripple reduction in the motor operation.

11 HENRIQUES et al.: PROPOSITION OF AN OFFLINE LEARNING CURRENT MODULATION 675 Fig. 12. PI controller + double external load for (a) no compensation and (b) compensation (vertical: 1 A/div and horizontal: 50 ms/div =40 mechanical degrees). (c) Compensation signal for I =4A. Frequency spectrum of the motor speed (d) before and (e) after compensation. VI. CONCLUSIONS An offline torque-ripple reduction scheme using a neuro-fuzzy compensation mechanism has been presented in this paper. The proposed technique adds a compensating signal to the PI speed controller, which has been offline learned by a neuro-fuzzy system. During the simulation tests, torque ripple was used as the training error variable. However, this approach would not be very practical for online implementation in a real system since

12 676 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 49, NO. 3, JUNE 2002 torque is a variable that is difficult to measure. For continuous online training, other variables could be more appropriate, such as acceleration or speed ripple, which are now being studied. However, the torque could still be used directly in an offline training system, e.g., for converter programming on a test rig at the factory. Simulation results of the switched reluctance drive have shown the good ripple reduction achieved by the incorporation of the compensating signal in the current waveform, which changes its shape according to the machine s operation. The presented offline technique has been tested experimentally. The results have shown clearly how the current has been modulated to reduce torque ripple for different motor speeds and load values. However, the applicability of the scheme considered in this paper is restricted due to the need for an offline training and the use of a torque sensor. To overcome this problem, the machine needs to be operated in a test-bench group using a torque sensor to allow the offline training of the compensator. In addition, since different SRMs have diverse inductance profiles because of the construction features and varied materials, the scheme proposed in this paper cannot be applied to a motor without a pretraining process to obtain the compensation function. The authors are currently researching the system with online training [10], [11]. ACKNOWLEDGMENT The authors would like to express their gratitude to CAPES/Brazil and ICCTI/ Portugal for their support. REFERENCES [1] S. Bolognani and M. Zigliotto, Fuzzy logic control of a switched reluctance motor drive, IEEE Trans. Ind. Applicat., vol. 32, pp , Sept./Oct [2] R. C. Kavanaugh, J. M. D. Murphy, and M. Egan, Torque ripple minimization in switched reluctance drives using self-learning techniques, in Proc. IEEE IECON 91, 1991, pp [3] D. S. Reay, M. M. Moud, T. C. Green, and B. W. Williams, Switched reluctance motor control via fuzzy adaptive systems, IEEE Contr. Syst. Mag., vol. 15, pp , June [4] J. Donovan, P. Roche, R. C. Kavanaugh, M. Egan, and J. M. D. Murphy, Neural network based torque ripple minimization in a switched reluctance motor, in Conf. Rec. IEEE-IAS Annu. Meeting, 1994, pp [5] S. Mir, M. E. Elbuluk, and I. Husain, Torque-ripple minimization in switched reluctance motors using adaptive fuzzy control, IEEE Trans. Ind. Applicat., vol. 35, pp , Mar./Apr [6] A. D. Cheok and N. Ertugrul, Use of fuzzy logic for modeling, estimation, and prediction in switched reluctance motor drives, IEEE Trans. Ind. Electron., vol. 46, pp , Dec [7] S. Russa, I. Husain, and M. E. Elbuluk, Torque-ripple minimization in switched reluctance machines over a wide speed range, IEEE Trans. Ind. Applicat., vol. 34, pp , Sept./Oct [8] J. M. L. Nascimento, L. G. B. Rolim, P. Heidrich, W. I. Suemitsu, and R. Hanitsch, Design and simulation results of a switched reluctance motor, in Proc. 3rd Brazilian Power Electronics Conf., COBEP 95, São Paulo, Brazil, Dec. 1995, pp [9] F. Soares and P. J. Costa Branco, Simulation of a 6/4 switched reluctance motor based on Matlab/Simulink environment, IEEE Trans. Aerosp. Electron. Syst., vol. 37, pp , July [10] L. O. P. Henriques, P. J. Costa Branco, L. G. Rolim, and W. I. Suemitsu, Automatic learning of pulse current shape for torque ripple minimization in switched reluctance machines, in Proc. European Control Conf., Porto, Portugal, Sept. 2001, pp [11] 6th Online World Conf. Soft Computing in Industrial Applications (WSC6) (2001, Sept.). [Online]. Available: [12] J. R. Jang, ANFIS: Adaptive-networks based fuzzy inference system, IEEE Trans. Syst., Man, Cybern., vol. 23, pp , May/June [13] S. Chiu, Fuzzy model identification based on cluster estimation, J. Intell. Fuzzy Syst., vol. 2, no. 3, Luis Oscar de Araujo Porto Henriques (S 95 M 99) was born in Juiz de Fora, Brazil, in He received the B.Sc. degree in electrical engineering in 1997 from the Federal University of Juiz de Fora, Juiz de Fora, Brazil, and the M.Sc. degree in electrical engineering and the Ph.D. degree in 1999 from the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. Since December 2000, he has been with the Instituto Superior Técnico, Lisbon, Portugal, where he is currently a Research Student. His main research interests include power electronics, electrical motor drives and intelligent control. Dr. Henriques is a member of the Brazilian Society for Automatic Control and Brazilian Power Electronics Society. P. J. Costa Branco (M 92) is currently an Assistant Professor in the Section of Electrical Machines and Power Electronics, Department of Electrical and Computing Engineering, Instituto Superior Técnico (IST), Lisbon, Portugal, where he has been with the Mechatronics Laboratory/DEEC-IST since His research interests include soft-computing techniques, and he is presently engaged in research on advanced learning control techniques for electromechanical systems. He has been a referee of international scientific journals and participated on the boards of international meetings. He has authored numerous articles published in international scientific journals such as the IEEE TRANSACTIONS ON MAGNETICS, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, IEEE TRANSACTIONS ON FUZZY SYSTEMS, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, Pattern Recognition Letters, Fuzzy Sets and Systems, and European Transactions on Electrical Power Engineering. Luís Guilherme Barbosa Rolim was born in Niterói, Brazil, in He received the B.S. and M.S. degrees from the Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil, and the Dr.-Ing. degree from the Technical University Berlin, Berlin, Germany, in 1989, 1993, and 1997, respectively, all in electrical engineering. Since 1990, he has been a Faculty Member of the Electrical Engineering Department, Escola Politécnica, UFRJ, where he teaches and conducts research on power electronics, drives, and microprocessor control. He is a member of the Power Electronics Research Group at COPPE/UFRJ and has authored more than 20 papers published in Brazilian and international conference proceedings and technical journals. Walter Issamu Suemitsu (M 81) received the Electrical Engineer degree in 1975 from the Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil, the M.Sc. degree in electrical engineering from COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, and the Doctor degree in electrical engineering from the Institut National Polytechnique de Grenoble, Grenoble, France. Since 1977, he has been teaching and conducting research in the Departamento de Eletrotécnica,Escola de Engenharia, UFRJ, where he is currently an Associate Professor. Since 1986, he has also been an Associate Professor in the Programa de Engenharia Elétrica, COPPE/UFRJ. His research interests include electric machine drives and applications of power electronic converters to electrical drives, in particular, applications of digital control using DSPs and of learning-based control methodologies, such fuzzy logic, neural networks, and neuro-fuzzy methods.